2024 Inference Jobs - Page 3

Job Summary Strategic & Leadership-Level GenAI Skills AI Solution Architecture Designing scalable GenAI systems (e.g. RAG pipelines multi-agent systems). Choosing between hosted APIs vs open-source models. Architecting hybrid systems (LLMs + traditional software). Model Evaluation & Selection Benchmarking models (e.g. GPT-4 Claude Mistral LLaMA). Understanding trade-offs: latency cost accuracy context length. Using tools like LM Evaluatio Responsibilities Strategic & Leadership-Level GenAI Skills AI Solution Architecture Designing scalable GenAI systems (e.g. RAG pipelines multi-agent systems). Choosing between hosted APIs vs open-source models. Architecting hybrid systems (LLMs + traditional software). Model Evaluation & Selection Benchmarking models (e.g. GPT-4 Claude Mistral LLaMA). Understanding trade-offs: latency cost accuracy context length. Using tools like LM Evaluation Harness OpenLLM Leaderboard etc. Enterprise-Grade RAG Systems Designing Retrieval-Augmented Generation pipelines. Using vector databases (Pinecone Weaviate Qdrant) with LangChain or LlamaIndex. Optimizing chunking embedding strategies and retrieval quality. Security Privacy & Governance Implementing data privacy access control and audit logging. Understanding risks: prompt injection data leakage model misuse. Aligning with frameworks like NIST AI RMF EU AI Act or ISO/IEC 42001. Cost Optimization & Monitoring Estimating and managing GenAI inference costs. Using observability tools (e.g. Arize WhyLabs PromptLayer). Token usage tracking and prompt optimization. Advanced Technical Skills Model Fine-Tuning & Distillation Fine-tuning open-source models using PEFT LoRA QLoRA. Knowledge distillation for smaller faster models. Using tools like Hugging Face Axolotl or DeepSpeed. Multi-Agent Systems Designing agent workflows (e.g. AutoGen CrewAI LangGraph). Task decomposition memory and tool orchestration. Toolformer & Function Calling Integrating LLMs with external tools APIs and databases. Designing tool-use schemas and managing tool routing. Team & Product Leadership GenAI Product Thinking Identifying use cases with high ROI. Balancing feasibility desirability and viability. Leading GenAI PoCs and MVPs. Mentoring & Upskilling Teams Training developers on prompt engineering LangChain etc. Establishing GenAI best practices and code reviews. Leading internal hackathons or innovation sprints.

About Turing Based in Palo Alto, California, Turing is one of the world's fastest-growing AI companies accelerating the advancement and deployment of powerful AI systems. Turing helps customers in two ways: working with the world's leading AI labs to advance frontier model capabilities in thinking, reasoning, coding, agentic behavior, multimodality, multilingualism, STEM and frontier knowledge; and leveraging that expertise to build real-world AI systems that solve mission-critical priorities for Fortune 500 companies and government institutions. Turing has received numerous awards, including Forbes's "One of America's Best Startup Employers," #1 on The Information's annual list of "Most Promising B2B Companies," and Fast Company's annual list of the "World's Most Innovative Companies." Turing's leadership team includes AI technologists from industry giants Meta, Google, Microsoft, Apple, Amazon, Twitter, McKinsey, Bain, Stanford, Caltech, and MIT. For more information on Turing, visit www.turing.com . For information on upcoming Turing AGI Icons events, visit go.turing.com/agi-icons . About Turing Based in Palo Alto, California, Turing is one of the world's fastest-growing AI companies accelerating the advancement and deployment of powerful AI systems. Turing helps customers in two ways: working with the world's leading AI labs to advance frontier model capabilities in thinking, reasoning, coding, agentic behavior, multimodality, multilingualism, STEM and frontier knowledge; and leveraging that expertise to build real-world AI systems that solve mission-critical priorities for Fortune 500 companies and government institutions. Turing has received numerous awards, including Forbes's "One of America's Best Startup Employers," #1 on The Information's annual list of "Most Promising B2B Companies," and Fast Company's annual list of the "World's Most Innovative Companies." Turing's leadership team includes AI technologists from industry giants Meta, Google, Microsoft, Apple, Amazon, Twitter, McKinsey, Bain, Stanford, Caltech, and MIT. For more information on Turing, visit www.turing.com. For information on upcoming Turing AGI Icons events, visit go.turing.com/agi-icons. Position Summary We are looking for a hands-on Data Science Manager to lead our Fulfillment Data Science team. In this role, you will help build foundational data infrastructure and deliver insights that directly shape Turing's vetting and fulfillment operations. This is a high-impact role for a technically skilled and collaborative leader who can scale data-driven products and workflows that serve Turing's 3M+ global talent cloud. Key Responsibilities Build and lead a high-performing team of data scientists and data analysts Drive actionable product and operational insights, opportunity analyses, and metric tracking to guide product direction and success Cultivate strong partnerships with cross-functional stakeholders from product, engineering, operations, design etc. Design and maintain core data pipelines and conduct hands-on analysis of large-scale data Translate complex analyses into compelling narratives and visualizations that drive executive and cross-functional decision-making Find a path to get things done despite roadblocks to get your work into the hands of users quickly and iteratively Enjoy working in a fast-paced product development cycle. Required Qualifications Bachelor's or Master's degree in a quantitative field (e.g., Statistics, Applied Math, Physics, Engineering) 8+ years of hands-on data science experience, including work with large-scale structured and unstructured data 3+ years of experience managing data science teams Technical Skills Strong SQL and Python programming skills Experience building data products using Google BigQuery and data visualization tools (Tableau, Looker, Sigma) Familiarity with experimentation, causal inference, or forecasting techniques Preferred Qualifications Experience building and deploying GenAI based tools in human-in-the-loop systems Prior work in fulfillment, marketplace, or operations-focused teams at a high-growth company or startup Advantages of joining Turing: Amazing work culture (Super collaborative & supportive work environment; 5 days a week) Awesome colleagues (Surround yourself with top talent from Meta, Google, LinkedIn etc. as well as people with deep startup experience) Competitive compensation Flexible working hours Full-time remote opportunity Don't meet every single requirement? Studies have shown that women and people of color are less likely to apply to jobs unless they meet every single qualification. Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace and celebrate authenticity, so if you're excited about this role but your past experience doesn't align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles. Advantages of joining Turing: Amazing work culture (Super collaborative & supportive work environment; 5 days a week) Awesome colleagues (Surround yourself with top talent from Meta, Google, LinkedIn etc. as well as people with deep startup experience) Competitive compensation Flexible working hours Full-time remote opportunity Don't meet every single requirement? Studies have shown that women and people of color are less likely to apply to jobs unless they meet every single qualification. Turing is proud to be an equal opportunity employer. We do not discriminate on the basis of race, religion, color, national origin, gender, gender identity, sexual orientation, age, marital status, disability, protected veteran status, or any other legally protected characteristics. At Turing we are dedicated to building a diverse, inclusive and authentic workplace and celebrate authenticity, so if you're excited about this role but your past experience doesn't align perfectly with every qualification in the job description, we encourage you to apply anyways. You may be just the right candidate for this or other roles. For applicants from the European Union, please review Turing's GDPR notice here.

System Administrator Job Description We aim to bring about a new paradigm in medical image diagnostics; providing intelligent, holistic, ethical, explainable and patient centric care. We are looking for innovative problem solvers who love solving problems. We want people who can empathize with the consumer, understand business problems, and design and deliver intelligent products. We are looking for a System Administrator to manage and optimize our on-premise and cloud infrastructure, ensuring reliability, security, and scalability for high-throughput AI workloads. As a System Administrator, you will be responsible for managing servers, storage, network, and compute infrastructure powering our AI development and deployment pipelines. You will ensure seamless handling of large medical imaging datasets (DICOM/NIfTI), maintain high availability for research and production systems Key Responsibilities Infrastructure & Systems Management Manage Linux-based servers, GPU clusters, and network storage for AI training and inference workloads. Configure and maintain message queue systems (RabbitMQ, ActiveMQ, Kafka) for large-scale, asynchronous AI pipeline execution. Set up and maintain service beacons and health checks to proactively monitor the state of critical services (XNAT pipelines, FastAPI endpoints, AI model inference servers). Maintain PACS integration, DICOM routing, and high-throughput data transfer for medical imaging workflows. Manage hybrid infrastructure (on-prem + cloud) including auto-scaling compute for large training tasks. Service Monitoring & Reliability Implement automated service checking for all production and development services using Prometheus, Grafana, or similar tools. Configure beacon agents to trigger alerts and self-healing scripts for service restarts when anomalies are detected. Set up log aggregation and anomaly detection to catch failures in AI processing pipelines early. Ensure 99.9% uptime for mission-critical systems and clinical services. Security & Compliance Enforce secure access control (IAM, VPN, RBAC, MFA) and maintain audit trails for all system activities. Ensure compliance with HIPAA, GDPR, ISO 27001 for medical data storage and transfer. Encrypt medical imaging data (DICOM/NIfTI) at rest and in transit. Automation & DevOps Develop automation scripts for service restarts, scaling GPU resources, and pipeline deployments. Work with DevOps teams to integrate infrastructure monitoring with CI/CD pipelines. Optimize AI pipeline orchestration with MQ-based task handling for scalable performance. Backup, Disaster Recovery & High Availability Manage data backup policies for medical datasets, AI model artifacts, and PostgreSQL/MongoDB databases. Implement failover systems for MQ brokers and imaging data services to ensure uninterrupted AI processing. Collaboration & Support Work closely with AI engineers and data scientists to optimize compute resource utilization. Support teams in troubleshooting infrastructure and service issues. Maintain license servers and specialized imaging software environments. Skills and Qualifications Required: 3+ years of Linux systems administration experience with a focus on service monitoring and high-availability environments. Experience with message queues (RabbitMQ, ActiveMQ, Kafka) for distributed AI workloads. Familiarity with beacons, service health monitoring, self-healing automation. Experience managing GPU clusters (NVIDIA CUDA, drivers, dockerized AI workflows). Hands-on with cloud platforms (AWS, GCP, Azure). Networking fundamentals (firewalls, VPNs, load balancers). Hands-on experience with GPU-enabled servers (NVIDIA CUDA, drivers, dockerized AI workflows). Experience managing large datasets (100GB–TB scale), preferably in healthcare or scientific research. Familiarity with cloud platforms (AWS EC2, S3, EKS or equivalents). Knowledge of cybersecurity best practices and compliance frameworks (HIPAA, ISO 27001). Preferred: Experience with PACS, XNAT, or medical imaging servers. Familiarity with Prometheus, Grafana, ELK stack, SaltStack beacons, or similar monitoring tools. Knowledge of Kubernetes or Docker Swarm for container orchestration. Basic scripting knowledge (Bash, Python) for task automation. Exposure to database administration (PostgreSQL, MongoDB). Scripting skills (Bash, Python, PowerShell) for automation and troubleshooting. Understanding of databases (PostgreSQL, MongoDB) used in AI pipelines. Education: BE/B Tech, MS/M Tech (will be a bonus) Experience: 3-5 Years Job Type: Full-time Work Location: In person

We’re Hiring: Machine Learning Engineer (Part-Time | Flexible Remote) Are you an experienced ML Engineer looking for a flexible, part-time opportunity to work on real-world impact projects? Join us in building intelligent systems that match candidates to projects using structured skills, assessments, and feedback data. This is your chance to own end-to-end ML pipelines and work on meaningful automation in the HRTech space — all on your own schedule. 🔍 Role Overview: We’re looking for an ML Engineer to architect and deploy predictive models that power candidate–project matching intelligence , leveraging structured applicant data. You'll design scalable ML workflows and inference pipelines on AWS . 🔧 Key Responsibilities: Build data pipelines to ingest & preprocess applicant data (skills, assessments, feedback) Engineer task-specific features and transformation logic Train predictive models (logistic regression, XGBoost, etc.) on SageMaker Automate batch ETL, retraining flows, and storage with AWS S3 Deploy inference endpoints with Lambda , and integrate with systems in production Monitor model drift, performance, and feedback loops for continuous learning Document architecture, workflows, and ensure explainability ✅ You’ll Need: 4–6+ years in ML/AI engineering roles Strong command of Python, scikit-learn, XGBoost , and feature engineering Proven experience with AWS ML stack : SageMaker, Lambda, S3 Hands-on SQL/NoSQL and automated data workflows Familiarity with CI/CD for ML (CodePipeline, CodeBuild, etc.) Ability to independently own schema, features, and model delivery Bachelor’s or Master’s in CS, Engineering, or related field ⭐ Bonus Points For: NLP experience (extracting features from feedback/comments) Familiarity with serverless architectures Background in recruitment, talent platforms, or skills-matching system 👉 Apply now or DM us to know more. #Hiring #MachineLearning #MLJobs #RemoteJobs #PartTime #AWS #HRTech #MLOps #AI #RecruitmentTech #SageMaker #FlexibleWork

Hi Connections, Urgent - Hiring for below role About the Role: We are seeking a seasoned and highly skilled MLOps Engineer to join our growing team. The ideal candidate will have extensive hands-on experience with deploying, monitoring, and retraining machine learning models in production environments. You will be responsible for building and maintaining robust and scalable MLOps pipelines using tools like MLflow, Apache Airflow, Kubernetes, and Databricks or Azure ML. A strong understanding of infrastructure-as-code using Terraform is essential. You will play a key role in operationalizing AI/ML systems and ensuring high performance, availability, and automation across the ML lifecycle. --- Key Responsibilities: · Design and implement scalable MLOps pipelines for model training, validation, deployment, and monitoring. · Operationalize machine learning models using MLflow, Airflow, and containerized deployments via Kubernetes. · Automate and manage ML workflows across cloud platforms such as Azure ML or Databricks. · Develop infrastructure using Terraform for consistent and repeatable deployments. · Trace API calls to LLMs, Azure OCR and Paradigm · Implement performance monitoring, alerting, and logging for deployed models using custom and third-party tools. · Automate model retraining and continuous deployment pipelines based on data drift and model performance metrics. · Ensure traceability, reproducibility, and auditability of ML experiments and deployments. · Collaborate with Data Scientists, ML Engineers, and DevOps teams to streamline ML workflows. · Apply CI/CD practices and version control to the entire ML lifecycle. · Ensure secure, reliable, and compliant deployment of models in production environments. --- Required Qualifications: · 5+ years of experience in MLOps, DevOps, or ML engineering roles, with a focus on production ML systems. · Proven experience deploying machine learning models using MLflow and workflow orchestration with Apache Airflow. · Hands-on experience with Kubernetes for container orchestration in ML deployments. · Proficiency with Databricks and/or Azure ML, including model training and deployment capabilities. · Solid understanding and practical experience with Terraform for infrastructure-as-code. · Experience automating model monitoring and retraining processes based on data and model drift. · Knowledge of CI/CD tools and principles applied to ML systems. · Familiarity with monitoring tools and observability stacks (e.g., Prometheus, Grafana, Azure Monitor). · Strong scripting skills in Python · Deep understanding of ML lifecycle challenges including model versioning, rollback, and scaling. · Excellent communication skills and ability to collaborate across technical and non-technical teams. --- Nice to Have: · Experience with Azure DevOps or GitHub Actions for ML CI/CD. · Exposure to model performance optimization and A/B testing in production environments. · Familiarity with feature stores and online inference frameworks. · Knowledge of data governance and ML compliance frameworks. · Experience with ML libraries like scikit-learn, PyTorch, or TensorFlow. --- Education: · Bachelor’s or Master’s degree in Computer Science, Engineering, Data Science, or a related field.

Location: Remote (India-based preferred) Type: Full-time | Founding Team | High Equity Company: Flickd (www.flickd.in) About the Role We’re building India’s most advanced virtual try-on engine — think Doji meets TryOnDiffusion, but optimized for real-world speed, fashion, and body diversity. As our ML Engineer (Computer Vision + Try-On) , you’ll own the end-to-end pipeline : from preprocessing user/product images to generating hyper-realistic try-on results with preserved pose, skin, texture, and identity. You’ll have full autonomy to build, experiment, and ship — working directly with React, Spring Boot, DevOps, and design folks already in place. This is not a junior researcher role. This is one person building the brain of the system - and setting the foundation for India's biggest visual shopping innovation. What You’ll Build Stage 1: User Image Preprocessing Human parsing (face, body, hair), pose detection, face/limb alignment Auto orientation, canvas resizing, brightness/contrast normalization Stage 2: Product Image Processing Background removal, garment segmentation (SAM/U^2-Net/YOLOv8) Handle occlusions, transparent clothes, long sleeves, etc. Stage 3: Try-On Engine Implement and iterate on CP-VTON / TryOnDiffusion / FlowNet Fine-tune on custom data for realism, garment drape, identity retention Inference Optimisation TorchScript / ONNX, batching, inference latency minimization Collaborate with DevOps for Lambda/EC2 + GPU deployment Postprocessing Alpha blending, edge smoothing, fake shadows, cloth-body warps You’re a Fit If You: Have 2–5 years in ML/CV with real shipped work (not just notebooks) Have worked on: human parsing, pose estimation, cloth warping, GANs Are hands-on with PyTorch , OpenCV, Segmentation Models, Flow or ViT Can replicate models from arXiv fast, and care about output quality Want to own a system seen by millions , not just improve metrics Stack You’ll Use PyTorch, ONNX, TorchScript, Hugging Face DensePose, OpenPose, Segment Anything, Diffusion Models Docker, Redis, AWS Lambda, S3 (infra is already set up) MLflow or DVC (can be implemented from scratch) For exceptional talent, we’re flexible on cash vs equity split. Why This Is a Rare Opportunity Build the core AI product that powers a breakout consumer app Work in a zero BS, full-speed team (React, SpringBoot, DevOps, Design all in place) Be the founding ML brain and shape all future hires Ship in weeks, not quarters — and see your output in front of users instantly Apply now, or DM Dheekshith (Founder) on LinkedIn with your GitHub or project links. Let’s build something India’s never seen before.

Job Description: 7–10 years of industry experience, with at least 5 years in machine learning roles. Advanced proficiency in Python and common ML libraries: TensorFlow, PyTorch, Scikit-learn. Experience with distributed training, model optimization (quantization, pruning), and inference at scale. Hands-on experience with cloud ML platforms: AWS (SageMaker), GCP (Vertex AI), or Azure ML. Familiarity with MLOps tooling: MLflow, TFX, Airflow, or Kubeflow; and data engineering frameworks like Spark, dbt, or Apache Beam. Strong grasp of CI/CD for ML, model governance, and post-deployment monitoring (e.g., data drift, model decay). Excellent problem-solving, communication, and documentation skills.

Ignite the Future of Language with AI at Teradata! What You'll Do: Shape the Way the World Understands Data At Teradata, we're not just managing data; we're unleashing its full potential. Our ClearScape Analytics™ platform and pioneering Enterprise Vector Store are empowering the world's largest enterprises to derive unprecedented value from their most complex data. We're rapidly pushing the boundaries of what's possible with Artificial Intelligence, especially in the exciting realm of autonomous and agentic systems We’re building intelligent systems that go far beyond automation — they observe, reason, adapt, and drive complex decision-making across large-scale enterprise environments. As a member of our AI engineering team, you’ll play a critical role in designing and deploying advanced AI agents that integrate deeply with business operations, turning data into insight, action, and measurable outcomes. You’ll work alongside a high-caliber team of AI researchers, engineers, and data scientists tackling some of the hardest problems in AI and enterprise software — from scalable multi-agent coordination and fine-tuned LLM applications, to real-time monitoring, drift detection, and closed-loop retraining systems. If you're passionate about building intelligent systems that are not only powerful but observable, resilient, and production-ready, this role offers the opportunity to shape the future of enterprise AI from the ground up. We are seeking a highly skilled Senior AI Engineer to drive the development and deployment of Agentic AI systems with a strong emphasis on AI observability and data platform integration. You will work at the forefront of cutting-edge AI research and its practical application—designing, implementing, and monitoring intelligent agents capable of autonomous reasoning, decision-making, and continuous learning. Who You'll Work With: Join Forces with the Best Imagine collaborating daily with some of the brightest minds in the company – individuals who champion diversity, equity, and inclusion as fundamental to our success. You'll be part of a cohesive force, laser-focused on delivering high-quality, critical, and highly visible AI/ML functionality within the Teradata Vantage platform. Your insights will directly shape the future of our intelligent data solutions. You'll report directly to the inspiring Sr. Manager, Software Engineering, who will champion your growth and empower your contributions. What Makes You a Qualified Candidate: Skills in Action Architect and implement Agentic AI systems capable of multi-step reasoning, tool use, and autonomous task execution. Build and maintain AI observability pipelines to monitor agent behavior, decision traceability, model drift, and overall system performance. Design and develop data platform components that support real-time and batch processing, data lineage, and high-availability systems for AI training and inference workflows. Integrate LLMs and multi-modal models into robust AI agents using frameworks like LangChain, OpenAI, Hugging Face, or custom stacks. Collaborate with product, research, and MLOps teams to ensure smooth integration between AI agents and user-facing applications Implement safeguards, feedback loops, and evaluation metrics to ensure AI safety, reliability, and compliance. Implement safeguards, feedback loops, and evaluation metrics to ensure AI safety, reliability, and compliance. Passion for staying current with AI research, especially in the areas of reasoning, planning, and autonomous systems. You are an excellent backend engineer who codes daily and owns systems end-to-end. Strong engineering background (Python/Java/Golang, API integration, backend frameworks) Strong system design skills and understanding of distributed systems. You’re obsessive about reliability, debuggability, and ensuring AI systems behave deterministically when needed. Hands-on experience with Machine learning & deep learning frameworks: TensorFlow, PyTorch, Scikit-learn Hands-on experience with LLMs, agent frameworks (LangChain, AutoGPT, ReAct, etc. ), and orchestration tools. Experience with AI observability tools and practices (e. g. , logging, monitoring, tracing, metrics for AI agents or ML models). Solid understanding of model performance monitoring, drift detection, and responsible AI principles. What You Bring: Passion and Potential A Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field – your academic foundation is key. A genuine excitement for AI and large language models (LLMs) is a significant advantage – you'll be working at the cutting edge! Design, develop, and deploy agentic systems integrated into the data platform. 5+ years of experience in software architecture, backend systems, or AI infrastructure. Strong experience with LLMs, transformers, and tools like OpenAI API, Anthropic Claude, or open-source LLMs. Deep understanding of AI observability (e. g. , tracing, monitoring, model explainability, drift detection, evaluation pipelines). Build dashboards and metrics pipelines to track key AI system indicators: latency, accuracy, tool invocation success, hallucination rate, and failure modes. Integrate observability tooling (e. g. , OpenTelemetry, Prometheus, Grafana) with LLM-based workflows and agent pipelines. Familiarity with modern data platform architecture Strong background in distributed systems, microservices, and cloud platforms (AWS, GCP, Azure). Experience in software development (Python, Go, or Java preferred). Familiarity with backend service development, APIs, and distributed systems. Familiarity with containerized environments (Docker, Kubernetes) and CI/CD pipelines. Bonus: Research experience or contributions to open-source agentic frameworks. You're knowledgeable about open-source tools and technologies and know how to leverage and extend them to build innovative solutions. Preferred Qualifications Experience with tools such as Arize AI, WhyLabs, Traceloop, or Prometheus + custom monitoring for AI/ML. Contributions to open-source agent frameworks or AI infra. Advanced degree (MS/PhD) in Computer Science, Artificial Intelligence, or related field. Experience working with multi-agent systems, real-time decision systems, or autonomous workflows. Why We Think You’ll Love Teradata We prioritize a people-first culture because we know our people are at the very heart of our success. We embrace a flexible work model because we trust our people to make decisions about how, when, and where they work. We focus on well-being because we care about our people and their ability to thrive both personally and professionally. We are an anti-racist company because our dedication to Diversity, Equity, and Inclusion is more than a statement. It is a deep commitment to doing the work to foster an equitable environment that celebrates people for all of who they are. Teradata invites all identities and backgrounds in the workplace. We work with deliberation and intent to ensure we are cultivating collaboration and inclusivity across our global organization. ​ We are proud to be an equal opportunity and affirmative action employer. We do not discriminate based upon race, color, ancestry, religion, creed, sex (including pregnancy, childbirth, breastfeeding, or related conditions), national origin, sexual orientation, age, citizenship, marital status, disability, medical condition, genetic information, gender identity or expression, military and veteran status, or any other legally protected status.

Line of Service Advisory Industry/Sector Not Applicable Specialism Data, Analytics & AI Management Level Director Job Description & Summary At PwC, our people in data and analytics engineering focus on leveraging advanced technologies and techniques to design and develop robust data solutions for clients. They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth. Those in artificial intelligence and machine learning at PwC will focus on developing and implementing advanced AI and ML solutions to drive innovation and enhance business processes. Your work will involve designing and optimising algorithms, models, and systems to enable intelligent decision-making and automation. Why PWC At PwC, you will be part of a vibrant community of solvers that leads with trust and creates distinctive outcomes for our clients and communities. This purpose-led and values-driven work, powered by technology in an environment that drives innovation, will enable you to make a tangible impact in the real world. We reward your contributions, support your wellbeing, and offer inclusive benefits, flexibility programmes and mentorship that will help you thrive in work and life. Together, we grow, learn, care, collaborate, and create a future of infinite experiences for each other. Learn more about us. At PwC, we believe in providing equal employment opportunities, without any discrimination on the grounds of gender, ethnic background, age, disability, marital status, sexual orientation, pregnancy, gender identity or expression, religion or other beliefs, perceived differences and status protected by law. We strive to create an environment where each one of our people can bring their true selves and contribute to their personal growth and the firm’s growth. To enable this, we have zero tolerance for any discrimination and harassment based on the above considerations Job Description & Summary: A career within Data and Analytics services will provide you with the opportunity to help organizations uncover enterprise insights and drive business results using smarter data analytics. We focus on a collection of organizational technology capabilities, including business intelligence, data management, and data assurance that help our clients drive innovation, growth, and change within their organizations in order to keep up with the changing nature of customers and technology. We make impactful decisions by mixing mind and machine to leverage data, understand and navigate risk, and help our clients gain a competitive edge. Responsibilities Strategic Leadership Define and execute the long-term roadmap for Agentic AI and GenAI initiatives. Identify and prioritize opportunities where autonomous agents and generative AI can drive business value. Collaborate with executive leadership to align AI strategy with overall business goals. Technology & Research Oversight Lead architecture and development of intelligent agent frameworks (e.g., goal-driven, tool-using agents). Oversee the application of LLMs, multimodal models, and fine-tuning strategies for domain-specific use cases. Evaluate and integrate emerging GenAI/Agentic technologies (e.g., AutoGPT, LangChain, ReAct, DSPy, etc.). Team Management & Collaboration Build, mentor, and scale a world-class team of AI researchers, ML engineers, and product managers. Foster a strong interdisciplinary culture of innovation and experimentation. Collaborate cross-functionally with data engineering, product, legal, and design teams. Operational Excellence Oversee data pipelines, model training, inference infrastructure, and model governance. Establish benchmarks and evaluation protocols for agent behavior, safety, and performance. Ensure ethical and responsible AI development practices are followed. Mandatory Skill Sets GenAI/Agentic technologies (e.g., AutoGPT, LangChain, ReAct, DSPy, etc.). Preferred Skill Sets Lang Experience in industries such as finance, consulting Track record of publishing or contributing to open-source AI frameworks. Understanding of regulatory, ethical, and societal implications of autonomous AI system Years Of Experience Required 14-17 Education Qualification B.Tech / M.Tech / MBA / MCA Education (if blank, degree and/or field of study not specified) Degrees/Field of Study required: Master of Business Administration, Master of Engineering, Bachelor of Engineering Degrees/Field Of Study Preferred Certifications (if blank, certifications not specified) Required Skills Generative AI Optional Skills Accepting Feedback, Accepting Feedback, Active Listening, AI Implementation, Analytical Thinking, C++ Programming Language, Coaching and Feedback, Communication, Complex Data Analysis, Creativity, Data Analysis, Data Infrastructure, Data Integration, Data Modeling, Data Pipeline, Data Quality, Deep Learning, Embracing Change, Emotional Regulation, Empathy, GPU Programming, Inclusion, Influence, Innovation, Intellectual Curiosity {+ 37 more} Desired Languages (If blank, desired languages not specified) Travel Requirements Not Specified Available for Work Visa Sponsorship? No Government Clearance Required? No Job Posting End Date

Job Summary Job Title: AI/ML Engineer Location: TechM Blr ECITY Years of Experience: 10+ Years Job Summary Chevron invites applications for the role of AI/ML Engineer within our Enterprise AI team in India. This position is integral to designing and developing AI/ML models that significantly accelerate the delivery of business value. We are looking for a Machine Learning Engineer with the ability to bring their expertise, innovative attitude, and excitement for solving complex problems with modern technologies and approaches. We seek individuals with a passion for exploring, innovating, and delivering innovative Data Science solutions that provide immense value to our business. Responsibilities Design and develop AI/ML models to enhance business processes and deliver actionable insights. Implement machine learning frameworks and libraries, ensuring robust model performance and scalability. Collaborate with cross functional teams to integrate AI solutions into existing workflows. Manage the lifecycle of machine learning models, including training, validation, deployment, and monitoring. Develop and maintain custom APIs for machine learning models to facilitate training and inference. Utilize Azure services to build and deploy machine learning pipelines effectively. Engage with technical experts to identify opportunities for applying machine learning and analytics. Communicate findings and insights clearly to stakeholders at all levels. Mandatory Skills Minimum 5 years of experience in Object Oriented Programming in Python. Proven experience with Azure IaaS services, particularly in building machine learning pipelines using Azure Machine Learning and/or Fabric. Strong understanding of software engineering principles, including source control, architecture, and testing methodologies. Experience with containers and container management (Docker, Kubernetes). Proficient in orchestrating large scale ML/DL jobs and leveraging Modern Data Platform tooling. Experience in designing custom APIs for machine learning models. Knowledge of mathematics (linear algebra, probability, Statistics) and algorithms. Ability to communicate effectively in both oral and written forms. Preferred Skills Experience implementing machine learning frameworks such as MLflow. Familiarity with Data Engineering and transformation tools like Azure Databricks, Spark, and Azure ADF. History of working with large scale model optimization and Neural Networks Hyper Parameter Tuning. Experience with unstructured data using Azure Cognitive Services and/or Computer Vision. Understanding of enterprise SaaS complexities, including security, scalability, and production support. Qualifications Bachelor's or Master's degree in Computer Science, Data Science, or a related field. 7 10 years of relevant experience in AI/ML engineering. Strong problem solving skills and a methodical approach to software design and development.

Our Company Changing the world through digital experiences is what Adobe’s all about. We give everyone—from emerging artists to global brands—everything they need to design and deliver exceptional digital experiences! We’re passionate about empowering people to create beautiful and powerful images, videos, and apps, and transform how companies interact with customers across every screen. We’re on a mission to hire the very best and are committed to creating exceptional employee experiences where everyone is respected and has access to equal opportunity. We realize that new ideas can come from everywhere in the organization, and we know the next big idea could be yours! Our Company At Adobe, you will be immersed in an outstanding work environment that is recognized around the world. Adobe has been consistently listed as Fortune’s “100 Best Companies to Work For” for 20 consecutive years. You will also be surrounded by colleagues who are dedicated to supporting each other's growth. If you’re looking to make a difference, Adobe's the place for you. Learn more about our employees' career experiences on the Adobe Life blog and explore the valuable benefits we provide. Changing the world through digital experiences is what Adobe’s all about. We give global brands everything they need to craft and deliver outstanding digital experiences. We’re passionate about redefining how companies connect with customers across every screen. Our goal is to recruit top talent and provide excellent employee experiences that prioritize respect and equal opportunity. We acknowledge that valuable insights can originate from any team member, including yourself. Adobe is seeking a highly motivated and versatile Research Scientist to join our Media and Data Science Research (MDSR) Laboratory in the Digital Experience (DX) business unit. This role provides an outstanding opportunity to engage in world-class research that will craft the future of digital experiences. As a part of our ambitious team, you will work on innovative research problems and implement solutions to redefine how businesses operate. As part of the research team, you will get an opportunity to turn your research ideas into successful products and at the same time, disseminate them at top conferences and journals. Responsibilities Design, implement, and optimize ML algorithms to address real-world problems in understanding user behavior and generating content to improve marketing performance. Develop scalable synthetic data generation and evaluation techniques. Manipulate and analyze complex, high-volume, high-dimensionality data from diverse sources. Train models and run experiments to study data and model quality. Iteratively refine research experiments and clearly communicate insights to the team. Integrate core machine learning models into the Adobe Experience Manager and Adobe Experience Platform. Collaborate with product, design, and engineering teams to rapidly prototype and transition conceptual prototypes from research to production. Requirements Proven expertise in one or more of the following areas: Large Language Models, Computer Vision, Natural Language Processing, Computational Social Science, Behavioral Sciences, Agentic Workflows, Social Network Analysis, Causal Inference, Recommendation Systems, Multimodal Content Understanding. Quality research work published in top conferences. Hands-on experience and a track record of deploying solutions in production environments. Experience in deriving concrete conclusions and actionable insights from large datasets. Ability to take research risks and solve hard problems independently. BTech (for research associates) or PhD degree or an equivalent experience (for research scientists) in industrial research in Computer Science, Statistics, Economics, or other relevant technical fields. You Will Thrive In This Role If You: Are a great teammate who is willing to take on a variety of tasks to ensure the success of the team Have a consistent track record for empirical research, answering complex questions with data. Have experience working in complex technical environments, improving research velocity. Possess a flexible analytic approach that allows for results at varying levels of precision. Have familiarity with relational databases, SQL and large-scale data systems such as Apache Spark. Adobe is proud to be an Equal Employment Opportunity employer. We do not discriminate based on gender, race or color, ethnicity or national origin, age, disability, religion, sexual orientation, gender identity or expression, veteran status, or any other applicable characteristics protected by law. Learn more. Adobe aims to make Adobe.com accessible to any and all users. If you have a disability or special need that requires accommodation to navigate our website or complete the application process, email accommodations@adobe.com or call (408) 536-3015.

You might be a fit if you have ● 5 + yrs production ML / data-platform engineering (Python or Go/Kotlin). ● Deployed agentic or multi-agent systems (e.g., micro-policy nets, bandit ensembles) and reinforcement-learning pipelines at scal (ad budget, recommender, or game AI). ● Fluency with BigQuery / Snowflake SQL & ML plus streaming (Kafka / Pub/Sub). ● Hands-on LLM fine-tuning using LoRA/QLoRA and proven prompt-engineering skills (system / assist hierarchies, few-shot, prompt compression). ● Comfort running GPU & CPU model serving on GCP (Vertex AI, GKE, or bare-metal K8s). ● Solid causal-inference experience (CUPED, diff-in-diff, synthetic control, uplift). ● CI/CD, IaC (Terraform or Pulumi) & observability chops (Prometheus, Grafana). ● Bias toward shipping working software over polishing research papers. Bonus points for: ● Postal/geo datasets, ad-tech, or martech domain exposure. ● Packaging RL models as secure micro-services. ● VPC-SC, NIST, or SOC-2 controls in a regulated data environment. ● Green-field impact – architect the learning stack from scratch. ● Moat-worthy data – 260 M+ US consumer graph tying offline & online behavior. ● Tight feedback loops – your models go live in weeks, optimizing large amounts of marketing spend daily.

Build the AI Reasoning Layer for Education We’re reimagining the core intelligence layer for education —tackling one of the most ambitious challenges in AI: subjective assessment automation and ultra-personalized learning at scale. This isn’t just another LLM application. We’re building a first-principles AI reasoning engine combining multi-modal learning, dynamic knowledge graphs, and real-time content generation . The goal? To eliminate billions of wasted hours in manual evaluation and create an AI that understands how humans learn . As a Founding AI Engineer , you’ll define and build this system from the ground up. You’ll work on problems few have attempted, at the bleeding edge of LLMs, computer vision, and generative reasoning. What You’ll Be Solving: Handwriting OCR at near-human accuracy: How can we push vision-language models to understand messy, real-world input from students? Real-time learner knowledge modeling: Can AI track and reason about what someone knows—and how they’re learning—moment to moment? Generative AI that teaches: How do we create dynamic video lessons that evolve in sync with a learner’s knowledge state? Scalable inference infrastructure: How do we optimize LLMs and multimodal models to support millions of learners in real time? What You’ll Be Building: Architect, deploy & optimize multi-modal AI systems—OCR, knowledge-state inference, adaptive content generation. Build reasoning engines that combine LLMs, retrieval, and learner data to dynamically guide learning. Fine-tune foundation models (LLMs, VLMs) and implement cutting-edge techniques (quantization, LoRA, RAG, etc.). Design production-grade AI systems: modular, scalable, and optimized for inference at global scale. Lead experiments at the frontier of AI research, publishing if desired. Tech Stack & Skills Must-Have: Deep expertise in AI/ML, with a focus on LLMs, multi-modal learning, and computer vision. Hands-on experience with OCR fine-tuning and handwritten text recognition Strong proficiency in AI frameworks: PyTorch, TensorFlow, Hugging Face, OpenCV. Experience in optimizing AI for production: LLM quantization, retrieval augmentation, and MLOps. Knowledge graphs and AI-driven reasoning systems experience Nice-to-Have: Experience with Diffusion Models, Transformers, and Graph Neural Networks (GNNs). Expertise in vector databases, real-time inference pipelines, and low-latency AI deployment. Prior experience in ed-tech, adaptive learning AI, or multi-modal content generation. Why This Role Is Rare Define the AI stack for a category-defining product at inception. Work with deep ownership across research, engineering, and infrastructure. Founding-level equity and influence in a high-growth company solving a $100B+ problem. Balance of cutting-edge research and real-world deployment. Solve problems that matter —not just academically, but in people’s lives. Who this role is for This is for builders at the edge—engineers who want to architect, not just optimize. Researchers who want their ideas shipped.If you want to: Push LLMs, CV, and multimodal models to their performance limits. Build AI that learns, reasons, and adapts like a human tutor. Shape the foundational AI layer for education

About Company, Droisys is an innovation technology company focused on helping companies accelerate their digital initiatives from strategy and planning through execution. We leverage deep technical expertise, Agile methodologies, and data-driven intelligence to modernize systems of engagement and simplify human/tech interaction. Amazing things happen when we work in environments where everyone feels a true sense of belonging and when candidates have the requisite skills and opportunities to succeed. At Droisys, we invest in our talent and support career growth, and we are always on the lookout for amazing talent who can contribute to our growth by delivering top results for our clients. Join us to challenge yourself and accomplish work that matters We are seeking a highly experienced Computer Vision Architect with deep expertise in Python to design and lead the development of cutting-edge vision-based systems. The ideal candidate will architect scalable solutions that leverage advanced image and video processing, deep learning, and real-time inference. You will collaborate with cross-functional teams to deliver high-performance, production-grade computer vision platforms. Key Responsibilities: Architect and design end-to-end computer vision solutions for real-world applications (e.g., object detection, tracking, OCR, facial recognition, scene understanding, etc.) Lead R&D initiatives and prototype development using modern CV frameworks(OpenCV, PyTorch, TensorFlow, etc.) Optimize computer vision models for performance, scalability, and deployment on cloud, edge, or embedded systems Define architecture standards and best practices for Python-based CV pipelines Collaborate with product teams, data scientists, and ML engineers to translate business requirements into technical solutions Stay updated with the latest advancements in computer vision, deep learning, and AI Mentor junior developers and contribute to code reviews, design discussions, and technical documentation Required Skills & Qualifications: Bachelor’s or Master’s degree in Computer Science, Electrical Engineering, or related field (PhD is a plus) 8+ years of software development experience, with 5+ years in computer vision and deep learning Proficient in Python and libraries such as OpenCV, NumPy, scikit-image, Pillow Experience with deep learning frameworks like PyTorch, TensorFlow, or Keras Strong understanding of CNNs, object detection (YOLO, SSD, Faster R-CNN), semantic segmentation, and image classification Knowledge of MLOps, model deployment strategies (e.g., ONNX, TensorRT), and containerization (Docker/Kubernetes) Experience working with video analytics, image annotation tools, and large-scale dataset pipelines Familiarity with edge deployment (Jetson, Raspberry Pi, etc.) or cloud AI services(AWS SageMaker, Azure ML, GCP AI) Droisys is an equal opportunity employer. We do not discriminate based on race, religion, color, national origin, gender, gender expression, sexual orientation, age, marital status, veteran status, disability status or any other characteristic protected by law. Droisys believes in diversity, inclusion, and belonging, and we are committed to fostering a diverse work environment.

About Us QuillBot is an AI-powered writing platform on a mission to reimagine writing. QuillBot provides over 50 million monthly active uses students, professionals, and educators with free online writing and research tools to help them become more effective, productive, and confident. The QuillBot team was built on the idea that learning how to write and use that knowledge is empowering. They want to automate the more time-consuming parts of writing so that users can focus on their craft. Whether you're writing essays, social media posts, or emails, QuillBot has your back. It has an array of productivity-enhancing tools that are already changing the way the world writes. In the recent chain of events, we were also acquired by CourseHero, which is a 15-year-old Ed-Tech unicorn based out of California, now known as Learneo. Overview QuillBot is looking for a hands-on MLOps Manager to lead and scale our AI Engineering & MLOps function. This role blends deep technical execution (60%) with team and cross-functional collaboration (40%), and is ideal for someone who thrives in a dual IC + strategic lead position. You'll work closely with Research, Platform, Infra, and Product teams — not only to deploy models reliably, but also to accelerate experimentation, training, and iteration cycles. From infra support for large-scale model training to scaling low-latency inference systems in production, you'll be at the heart of how AI ships at QuillBot. Responsibilities Own the full ML lifecycle: from training infra and experiment tracking to deployment, observability, and optimization. Work closely with researchers to remove friction in training, evaluation, and finetuning workflows. Guide and mentor a small, mature team of engineers (3–4), while still contributing as an individual contributor. Drive performance optimization (latency, throughput, cost efficiency), model packaging, and runtime reliability. Build robust systems for CI/CD, versioning, rollback, A/B testing, monitoring, and alerting. Ensure scalable, secure, and compliant AI infrastructure across training and inference environments. Collaborate with cloud and AI providers (e.g., AWS, GCP, OpenAI) as needed to integrate tooling, optimize costs, and unlock platform capabilities. Contribute to other GenAI and cross-functional AI initiatives as needed, beyond core MLOps responsibilities. Contribute to architectural decisions, roadmap planning, and documentation of our AI engineering stack. Champion automation, DevOps/MLOps best practices, and technical excellence across the ML lifecycle. Qualifications 5+ years of strong experience in MLOps, ML/AI Engineering. Solid understanding of ML/DL fundamentals and applied experience in model deployment and training infra. Proficient with cloud-native ML tooling (e.g., GCP, Vertex AI, Kubernetes). Comfortable working on both training-side infra and inference-side systems. Good to have experience with model optimization techniques (e.g., quantization, distillation, FasterTransformer, TensorRT-LLM). Proven ability to lead complex technical projects end-to-end with minimal oversight. Strong collaboration and communication skills — able to work cross-functionally and drive technical clarity. Ownership mindset — comfortable making decisions and guiding others in ambiguous problem spaces." Benefits & Perks Competitive salary, stock options & annual bonus Medical coverage Life and accidental insurance Vacation & leaves of absence (menstrual, flexible, special, and more!) Developmental opportunities through education & developmental reimbursements & professional workshops Maternity & parental leave Hybrid & remote model with flexible working hours On-site & remote company events throughout the year Tech & WFH stipends & new hire allowances Employee referral program Premium access to QuillBot Benefits and benefit amounts differ by region. A comprehensive list applicable to your region will be provided in your interview process. Research shows that candidates from underrepresented backgrounds often don't apply for roles if they don't meet all the criteria. We strongly encourage you to apply if you're interested: we'd love to learn how you can amplify our team with your unique experience! This role is eligible for hire in India. We are a virtual-first company and have employees dispersed throughout the United States, Canada, India and the Netherlands. We have a market-based pay structure that varies by location. The base pay for this position is dependent on multiple factors, including candidate experience and expertise, and may vary from the amounts listed. You may also be eligible to participate in our bonus program and may be offered benefits, and other types of compensation. #QuillBot Equal Employment Opportunity Statement (EEO) We are an equal opportunity employer and value diversity and inclusion within our company. We will consider all qualified applicants without regard to race, religion, color, national origin, sex, gender identity, gender expression, sexual orientation, age, marital status, veteran status, or ability status. We will ensure that individuals who are differently abled are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment as provided to other applicants or employees. Please contact us to request accommodation.

About Us QuillBot is an AI-powered writing platform on a mission to reimagine writing. QuillBot provides over 50 million monthly active uses students, professionals, and educators with free online writing and research tools to help them become more effective, productive, and confident. The QuillBot team was built on the idea that learning how to write and use that knowledge is empowering. They want to automate the more time-consuming parts of writing so that users can focus on their craft. Whether you're writing essays, social media posts, or emails, QuillBot has your back. It has an array of productivity-enhancing tools that are already changing the way the world writes. In the recent chain of events, we were also acquired by CourseHero, which is a 15-year-old Ed-Tech unicorn based out of California, now known as Learneo. Overview QuillBot is looking for a hands-on MLOps Manager to lead and scale our AI Engineering & MLOps function. This role blends deep technical execution (60%) with team and cross-functional collaboration (40%), and is ideal for someone who thrives in a dual IC + strategic lead position. You'll work closely with Research, Platform, Infra, and Product teams — not only to deploy models reliably, but also to accelerate experimentation, training, and iteration cycles. From infra support for large-scale model training to scaling low-latency inference systems in production, you'll be at the heart of how AI ships at QuillBot. Responsibilities Own the full ML lifecycle: from training infra and experiment tracking to deployment, observability, and optimization. Work closely with researchers to remove friction in training, evaluation, and finetuning workflows. Guide and mentor a small, mature team of engineers (3–4), while still contributing as an individual contributor. Drive performance optimization (latency, throughput, cost efficiency), model packaging, and runtime reliability. Build robust systems for CI/CD, versioning, rollback, A/B testing, monitoring, and alerting. Ensure scalable, secure, and compliant AI infrastructure across training and inference environments. Collaborate with cloud and AI providers (e.g., AWS, GCP, OpenAI) as needed to integrate tooling, optimize costs, and unlock platform capabilities. Contribute to other GenAI and cross-functional AI initiatives as needed, beyond core MLOps responsibilities. Contribute to architectural decisions, roadmap planning, and documentation of our AI engineering stack. Champion automation, DevOps/MLOps best practices, and technical excellence across the ML lifecycle. Qualifications 5+ years of strong experience in MLOps, ML/AI Engineering. Solid understanding of ML/DL fundamentals and applied experience in model deployment and training infra. Proficient with cloud-native ML tooling (e.g., GCP, Vertex AI, Kubernetes). Comfortable working on both training-side infra and inference-side systems. Good to have experience with model optimization techniques (e.g., quantization, distillation, FasterTransformer, TensorRT-LLM). Proven ability to lead complex technical projects end-to-end with minimal oversight. Strong collaboration and communication skills — able to work cross-functionally and drive technical clarity. Ownership mindset — comfortable making decisions and guiding others in ambiguous problem spaces." Benefits & Perks Competitive salary, stock options & annual bonus Medical coverage Life and accidental insurance Vacation & leaves of absence (menstrual, flexible, special, and more!) Developmental opportunities through education & developmental reimbursements & professional workshops Maternity & parental leave Hybrid & remote model with flexible working hours On-site & remote company events throughout the year Tech & WFH stipends & new hire allowances Employee referral program Premium access to QuillBot Benefits and benefit amounts differ by region. A comprehensive list applicable to your region will be provided in your interview process. Research shows that candidates from underrepresented backgrounds often don't apply for roles if they don't meet all the criteria. We strongly encourage you to apply if you're interested: we'd love to learn how you can amplify our team with your unique experience! This role is eligible for hire in India. We are a virtual-first company and have employees dispersed throughout the United States, Canada, India and the Netherlands. We have a market-based pay structure that varies by location. The base pay for this position is dependent on multiple factors, including candidate experience and expertise, and may vary from the amounts listed. You may also be eligible to participate in our bonus program and may be offered benefits, and other types of compensation. #Learneo Equal Employment Opportunity Statement (EEO) We are an equal opportunity employer and value diversity and inclusion within our company. We will consider all qualified applicants without regard to race, religion, color, national origin, sex, gender identity, gender expression, sexual orientation, age, marital status, veteran status, or ability status. We will ensure that individuals who are differently abled are provided reasonable accommodation to participate in the job application or interview process, to perform essential job functions, and to receive other benefits and privileges of employment as provided to other applicants or employees. Please contact us to request accommodation. About Learneo Learneo is a platform of builder-driven businesses, including Course Hero, CliffsNotes, LitCharts, Quillbot, Symbolab, and Scribbr, all united around a shared mission of supercharging productivity and learning for everyone. We attract and scale high growth businesses built and run by visionary entrepreneurs. Each team innovates independently but has a unique opportunity to collaborate, experiment, and grow together, and they are supported by centralized corporate operations functions, including HR, Finance and Legal.

Job Title: AI Research Engineer Intern (Fresher) Reporting to: Lead – Research & Innovation Lab Location: remote/ Hybrid (Chennai, India) Engagement: 6-month, full-time paid internship with pre-placement-offer track 1. Why this role exists Stratsyn AI Technology Services is turbo-charging Stratsyn’s cloud-native Enterprise Intelligence & Management Suite —a modular SaaS ecosystem that fuses advanced AI, low-code automation, multimodal search, and next-generation “Virtual workforce” agents. The platform unifies strategic planning, document intelligence, workflow orchestration, and real-time analytics, empowering C-suite leaders to simulate scenarios, orchestrate execution, and convert insight into action with unmatched speed and scalability. To keep pushing that frontier, we need sharp, curious minds who can translate cutting-edge research into production-grade capabilities for this suite. This internship is our talent-funnel into future Research Engineer and Product Scientist roles. 2. What you’ll do (core responsibilities) % FocusKey Responsibility 30 %Rapid Prototyping & Experimentation – implement state-of-the-art papers (LLMs, graph learning, causal inference, agents), design ablation studies, benchmark against baselines, and iterate fast. 25 %Data Engineering for Research – build reproducible datasets, craft synthetic data when needed, automate ETL pipelines, and enforce experiment tracking (MLflow / Weights & Biases). 20 %Model Evaluation & Explainability – create evaluation harnesses (BLEU, ROUGE, MAPE, custom KPIs), visualize error landscapes, and generate executive-ready insights. 15 %Collaboration & Documentation – author tech memos, well-annotated notebooks, and contribute to internal knowledge bases; present findings in weekly research stand-ups. 10 %Innovation Scouting – scan arXiv, ACL, NeurIPS, ICML, and startup ecosystems; summarize high-impact research and propose areas for IP creation within the Suite. 3. What you will learn / outcomes to achieve Master the end-to-end research workflow: literature review → hypothesis → prototype → validation → deployment shadow. Deliver one peer-review-quality technical report and two production-grade proof-of-concepts for the Suite. Achieve a measurable impact (e.g., 8-10 % forecasting-accuracy lift or 30 % latency reduction) on a live micro-service. 4. Minimum qualifications (freshers welcome) B.E./B.Tech/M.Sc./M.Tech in CS, Data Science, Statistics, EE, or related (2024-2026 pass-out). Fluency in Python and at least one deep-learning framework (PyTorch preferred). Solid grasp of linear algebra, probability, optimization, and algorithms. Hands-on academic or personal projects in NLP, CV, time-series, or RL (GitHub links highly valued). 5. Preferred extras Publications or Kaggle/ML-competition record. Experience with distributed training (GPU clusters, Ray, Lightning) and experiment-tracking tools. Familiarity with MLOps (Docker, CI/CD, Kubernetes) or data-centric AI. Domain knowledge in supply-chain, fintech, climate, or marketing analytics. 6. Key attributes & soft skills First-principles thinker – questions assumptions, proposes novel solutions. Bias for action – prototypes in hours, not weeks; embraces agile experimentation. Storytelling ability – explains complex models in clear, executive-friendly language. Ownership mentality – treats the prototype as a product, not just a demo. 7. Tech stack you’ll touch Python | PyTorch | Hugging Face | TensorRT | LangChain | Neo4j/GraphDB | PostgreSQL | Airflow | MLflow | Weights & Biases | Docker | GitHub Actions | JAX (exploratory) 8. Internship logistics & perks Competitive monthly stipend + performance bonus. High-end workstation + GPU credits on our private cloud. Dedicated mentor and 30-60-90-day learning plan. Access to premium research portals and paid conference passes. Culture of radical candor, weekly brown-bag tech talks, and hack days. Fast-track to full-time AI Research Engineer upon successful completion. 9. Application process Apply via email: Send résumé, brief statement of purpose, and GitHub/portfolio links to HR@stratsyn.ai . Online coding assessment: algorithmic + ML fundamentals. Technical interview (2 rounds): deep dive into projects, math, and research reasoning. Culture-fit discussion: with Research Lead & CPO. Offer & onboarding – target turnaround < 3 weeks.

The Data Scientist is crucial in leveraging data to derive meaningful insights and solutions for complex business problems. This individual will lead and guide the data science team in developing advanced analytical models, algorithms, and statistical analyses. They will collaborate with cross-functional teams to identify opportunities for leveraging data-driven solutions, making strategic decisions, and enhancing overall business performance. The Data Scientist will be responsible for designing and implementing machine learning models, conducting data exploration, and communicating findings to non-technical stakeholders. Primary Responsibilities Collaborate with business stakeholders to understand and translate their goals into AI and data science initiatives. Lead the development and implementation of LLM-based workflows and AI agents to drive automation, personalization, and intelligent decision-making. Develop strategies to optimize budget allocation and campaign performance using AI-driven approaches in scenarios with high cardinality and uncertainty. Conduct exploratory data analysis to uncover trends and insights from large, complex data sets. Identify, evaluate, and deploy use case-specific LLMs (e. g., OpenAI, Gemini, Claude) for summarization, retrieval, semantic search, tool use, and classification. Design and implement retrieval-augmented generation (RAG) pipelines and memory-augmented AI agents. Implement Groq or similar platforms for high-performance, low-latency inference and scalable AI deployment. Communicate complex analytical and AI-driven findings in a clear, actionable manner to non-technical stakeholders. Stay abreast of the latest advancements in AI, LLMs, and agentic systems. Build AI-powered proof of concepts (PoCs) leveraging foundation models, vector databases, and orchestration frameworks. Use SQL for data exploration, feature engineering, and prompt conditioning. Required Skills Qualification in a quantitative field such as Computer Science, Artificial Intelligence, Statistics, Physics, or Mathematics. Excellent problem-solving skills and strategic thinking with a strong AI product mindset. Strong coding skills, particularly in Python. 4-6 years of relevant work experience in Data Science, with significant hands-on experience in LLM-based application development. Solid foundation in statistical analysis, experimentation, and hypothesis testing. Proficiency in Python and SQL. Preferred experience on the GCP platform. Proven experience with LLMs, including prompt engineering, fine-tuning, RAG, and evaluation. Experience identifying and scaling LLM-based use cases across business functions. Familiar with building AI agents using LangChain, LangGraph, and agentic orchestration frameworks. Experience with Groq or similar platforms for high-speed inference of LLMs. Experience with LangSmith (LLMOps) for debugging and monitoring LLM workflows. Hands-on experience with fine-tuning LLMs for domain-specific applications. Practical experience in classical machine learning techniques and working with large-scale datasets. Plus points for experience in AdTech or Meta Ads. Effective communication skills with the ability to convey technical concepts to non-technical audiences. This job was posted by Shajy Theyyamveettil from Affle.

About the Company Based in Palo Alto, California, Turing is one of the world's fastest-growing AI companies accelerating the advancement and deployment of powerful AI systems. Turing helps customers in two ways: working with the world’s leading AI labs to advance frontier model capabilities in thinking, reasoning, coding, agentic behavior, multimodality, multilingualism, STEM and frontier knowledge; leveraging that expertise to build real-world AI systems that solve mission-critical priorities for Fortune 500 companies and government institutions. Turing has received numerous awards, including Forbes's "One of America's Best Startup Employers," #1 on The Information's annual list of "Most Promising B2B Companies," and Fast Company's annual list of the "World's Most Innovative Companies." Turing's leadership team includes AI technologists from industry giants Meta, Google, Microsoft, Apple, Amazon, Twitter, McKinsey, Bain, Stanford, Caltech, and MIT. For more information on Turing, visit www.turing.com. For information on upcoming Turing AGI Icons events, visit go.turing.com/agi-icons . About the R oleWe are looking for a hands-on Data Science Manager to lead our Fulfillment Data Science team. In this role, you will help build foundational data infrastructure and deliver insights that directly shape Turing’s vetting and fulfillment operations. This is a high-impact role for a technically skilled and collaborative leader who can scale data-driven products and workflows that serve Turing’s 3M+ global talent clo ud. Responsibil itiesBuild and lead a high-performing team of data scientists and data ana lystsDrive actionable product and operational insights, opportunity analyses, and metric tracking to guide product direction and su ccessCultivate strong partnerships with cross-functional stakeholders from product, engineering, operations, design etc.Design and maintain core data pipelines and conduct hands-on analysis of large-scale dataTranslate complex analyses into compelling narratives and visualizations that drive executive and cross-functional decision-m akingFind a path to get things done despite roadblocks to get your work into the hands of users quickly and iterat ivelyEnjoy working in a fast-paced product development c ycle. Qualifi cationsBachelor’s or Master’s degree in a quantitative field (e.g., Statistics, Applied Math, Physics, Engin eering)8+ years of hands-on data science experience, including work with large-scale structured and unstructur ed data3+ years of experience managing data scienc e teams Requir ed SkillsStrong SQL and Python programmi ng skillsExperience building data products using Google BigQuery and data visualization tools (Tableau, Looke r, Sigma)Familiarity with experimentation, causal inference, or forecasting t echniques Prefe rred SkillsExperience building and deploying GenAI based tools in human-in-the-l oop systemsPrior work in fulfillment, marketplace, or operations-focused teams at a high-growth company or startup

Welcome to Warner Bros. Discovery… the stuff dreams are made of. Who We Are… When we say, “the stuff dreams are made of,” we’re not just referring to the world of wizards, dragons and superheroes, or even to the wonders of Planet Earth. Behind WBD’s vast portfolio of iconic content and beloved brands, are the storytellers bringing our characters to life, the creators bringing them to your living rooms and the dreamers creating what’s next… From brilliant creatives, to technology trailblazers, across the globe, WBD offers career defining opportunities, thoughtfully curated benefits, and the tools to explore and grow into your best selves. Here you are supported, here you are celebrated, here you can thrive. Your New Role As Director – Analytics and Insights, you will architect and lead the future of data-driven decision-making at Warner Bros. Discovery. In this high-impact, global leadership role, you will own the strategy, delivery, and enterprise scaling of advanced analytics, business insights, and next-generation business intelligence (BI) platforms. You will be responsible for establishing a modern, intelligent insights ecosystem that empowers thousands of business users to make faster, smarter, and more strategic decisions—at scale. Your remit spans traditional analytics, enterprise BI (Tableau, Power BI, MicroStrategy, SAP BO), self-serve insights, conversational BI, and AI-augmented analytics, all under a single, unified vision. This role demands a visionary leader who can blend data architecture with business intuition, scale operational excellence, and drive measurable business value through insights. You will directly influence how WBD leverages data to optimize global operations, delight audiences, and accelerate innovation across its vast content and media ecosystem. Enterprise Analytics & Insights Strategy Develop and execute a unified enterprise strategy for analytics and insights—anchored in business impact, speed-to-decision, and user empowerment. Define and lead strategic programs that embed analytics into content optimization, audience engagement, operational efficiency, marketing attribution, and revenue management. Transform the organization’s decision-making from reactive reporting to proactive, predictive, and prescriptive insights. Business Intelligence Platform Leadership Own the global BI & reporting function, overseeing development, governance, performance, and innovation across platforms like Power BI, Tableau, MicroStrategy, and SAP BusinessObjects. Lead the modernization and consolidation of BI tools and workflows to drive consistency, scalability, and cost-efficiency. Establish enterprise-wide KPI frameworks, reporting templates, and executive dashboards to align operational and strategic decision-making. AI-Enabled, Self-Service & Conversational BI Spearhead the implementation of AI-powered BI, including self-service analytics, conversational interfaces, and natural language querying. Drive adoption of augmented analytics capabilities—automated insights, anomaly detection, forecast narratives, and proactive alerts. Enable a federated, insight-driven culture by designing intuitive, role-based BI experiences for business users, creatives, and executives alike. Cross-Functional Leadership & Stakeholder Engagement Act as a strategic advisor to senior leadership, translating complex analytics into actionable business insights with measurable outcomes. Partner closely with data engineering, data governance, data science, product, content, ad sales, marketing, and finance teams to ensure alignment and impact. Embed a data-first mindset across the enterprise through training, advocacy, and thought leadership. People Leadership & Operational Excellence Lead, inspire, and grow a high-performing global team of analysts, BI developers, product owners, and insight consultants. Institutionalize delivery excellence by building reusable assets, scalable reporting templates, and high-impact insight playbooks. Monitor performance and adoption of analytics solutions and drive continuous improvement. Innovation, Governance & Future Readiness Champion innovation in decision intelligence by exploring cutting-edge techniques such as causal inference, simulation modeling, and AI-generated narratives. Define and enforce BI governance, access controls, and data quality frameworks to maintain integrity, trust, and compliance. Stay at the forefront of analytics trends and tools, ensuring WBD’s analytics stack evolves ahead of the curve. Qualifications & Experiences Master’s in Business Analytics, Data Science, Computer Science, Statistics, Economics, or related field. 10+ years of progressive experience in analytics, insights, and BI—ideally within media, entertainment, or direct-to-consumer businesses. At least 5+ years of proven leadership experience in managing large, global teams across analytics and BI disciplines. Deep expertise in enterprise BI platforms such as Power BI, Tableau, MicroStrategy, SAP BusinessObjects, and experience leading BI modernization programs. Proven success in deploying AI-enabled analytics, augmented BI, conversational interfaces, and self-serve data platforms at scale. Strong understanding of modern cloud data stacks (Snowflake, BigQuery), data modeling, SQL, and analytical architecture. Exceptional executive communication, data storytelling, and stakeholder management skills. A strategic thinker with a hands-on ability to connect analytics execution with tangible business results. How We Get Things Done… This last bit is probably the most important! Here at WBD, our guiding principles are the core values by which we operate and are central to how we get things done. You can find them at www.wbd.com/guiding-principles/ along with some insights from the team on what they mean and how they show up in their day to day. We hope they resonate with you and look forward to discussing them during your interview. Championing Inclusion at WBD Warner Bros. Discovery embraces the opportunity to build a workforce that reflects a wide array of perspectives, backgrounds and experiences. Being an equal opportunity employer means that we take seriously our responsibility to consider qualified candidates on the basis of merit, regardless of sex, gender identity, ethnicity, age, sexual orientation, religion or belief, marital status, pregnancy, parenthood, disability or any other category protected by law. If you’re a qualified candidate with a disability and you require adjustments or accommodations during the job application and/or recruitment process, please visit our accessibility page for instructions to submit your request.

We are seeking a highly skilled and experienced Senior AI Engineer to lead the design, development, and deployment of advanced AI systems. You will work on cutting-edge machine learning models, natural language processing, computer vision, and AI infrastructure to solve real-world problems and drive innovation across our products and services. Key Responsibilities: Design, develop, and deploy scalable AI/ML models for production environments. Lead end-to-end AI project lifecycles from data collection and preprocessing to model training, evaluation, and deployment. Collaborate with cross-functional teams including data scientists, software engineers, and product managers. Optimize model performance and ensure robustness, fairness, and explainability. Stay current with the latest research and advancements in AI and machine learning. Mentor junior engineers and contribute to building a strong AI engineering culture. Required Qualifications: Bachelor’s or Master’s degree in Computer Science, Artificial Intelligence, Machine Learning, or a related field (PhD preferred). 5+ years of experience in AI/ML engineering with a strong portfolio of deployed models. Proficiency in Python and ML libraries such as TensorFlow, PyTorch, Scikit-learn, etc. Experience with cloud platforms (AWS, Azure) and MLOps tools. Strong understanding of data structures, algorithms, and software engineering principles. Excellent problem-solving and communication skills. Preferred Qualifications: Experience with LLMs, RAG, or agentic AI (Crew AI) systems. Familiarity with vector databases, prompt engineering, and AI safety practices. Contributions to open-source AI projects or published research papers. Experience with real-time inference systems and edge AI.

Location: Remote Employment Type: Full-time About the Role We are looking for a Senior Machine Learning Engineer to lead the development and deployment of AI/ML models for our platforms. In this role, you will drive technical strategy and you will be responsible for designing and deploying intelligent systems ,mentor junior engineers, and collaborate with cross-functional teams to deliver scalable, production-grade ML solutions. Key Responsibilities Independently design, build, and deploy machine learning models for core use cases. Drive the end-to-end lifecycle of ML projects—from scoping and architecture to implementation, deployment, and performance tuning. Maintain a hands-on approach in all aspects of development—from data preprocessing and feature engineering to model training, evaluation, and optimization. Lead technical reviews, provide constructive feedback, and help grow the team’s skill sets through coaching and knowledge sharing. Provide technical leadership and mentorship to junior engineers and data scientists, fostering a collaborative and high-performing team culture. Drive ML initiatives from ideation through production, ensuring scalability, performance, and maintainability. Collaborate with cross-functional teams including product, engineering, and operations to integrate intelligent solutions into user-facing products. Establish and promote ML best practices, including reproducibility, version control, testing,MLOps, and data governance. Oversee and guide the creation of scalable and maintainable ML pipelines and infrastructures. Stay ahead of industry trends and guide the adoption of new tools and techniques where relevant. Evaluate and integrate cutting-edge tools, frameworks, and techniques in NLP, deep learning, and computer vision. Own the quality, fairness, and compliance of ML systems, especially in sensitive use cases like content filtering and moderation. Design and implement machine learning models for automated content moderation , including toxicity, hate speech, spam, and NSFW detection. Build and optimize personalized recommendation systems using collaborative filtering, content-based, and hybrid approaches. Develop and maintain embedding-based similarity search for recommending relevant content based on user behavior and content metadata. Fine-tune and apply LLMs for moderation and summarization , leveraging prompt engineering or adapter-based methods. Deploy real-time inference pipelines for immediate content filtering and user-personalized suggestions. Ensure content moderation models are explainable , auditable , and bias-mitigated to align with ethical AI practices. Hands-on experience in content recommendation systems (e.g., collaborative filtering, ranking models, embeddings). Experience with content moderation frameworks , such as Perspective API, OpenAI moderation endpoints, or custom NLP classifiers. Strong knowledge of transformer-based models for NLP , including experience with Hugging Face, BERT, RoBERTa, etc. Practical experience with LLMs (GPT, Claude, Mistral) and tools for LLM fine-tuning or prompt engineering for moderation tasks. Familiarity with vector databases (e.g., FAISS, Pinecone) for similarity search in recommendation systems. Deep understanding of model fairness, debiasing techniques , and AI safety in content moderation . Required Skills & Qualifications Bachelor’s or Master’s degree in Computer Science, Machine Learning, Artificial Intelligence, or a related field. 5+ years of hands-on experience in machine learning, NLP, or deep learning, with a track record of leading projects. Expertise in Python and machine learning frameworks such as TensorFlow, PyTorch, Scikit-learn, Hugging Face, etc. Strong background in recommendation systems, content moderation, or ranking algorithms. Experience with cloud platforms (AWS/GCP/Azure), distributed computing (Spark), and MLOps tools. Proven ability to lead complex ML projects and teams, delivering business value through intelligent systems. Excellent communication skills, with the ability to explain complex ML concepts to stakeholders. Experience with LLMs (GPT, Claude, Mistral) and fine-tuning for domain-specific tasks. Knowledge of reinforcement learning, graph ML, or multimodal systems. Previous experience building AI systems for content moderation, personalization, or recommendation in a high-scale platform. Strong awareness of ethical AI principles, fairness, bias mitigation, and responsible data usage. Contributions to open-source ML projects or published research.

What You’ll Do Design, develop and deploy agent-based AI systems using LLMs Build and scale Retrieval-Augmented Generation pipelines for real-time and offline inference. Develop and optimize training workflows for fine-tuning and adapting models to domain-specific tasks. Collaborate with cross-functional teams to integrate knowledge base into agent frameworks. Drive best practices in AI Engineering, model lifecycle management, and production deployment on Google Cloud (GCP) Implement version control strategies using Git, manage code repositories and ensure best practices in code management. Develop, manage CI/CD pipelines using Jenkins or other relevant tools to streamline deployment and updates. Monitor, evaluate, and improve model performance post- deployment on Google Cloud. Communicate technical findings and insights to non-technical stakeholders. Participate in technical discussions and contribute to strategic planning. What Experience You Need Master's / Bachelors in Computer Science, Artificial Intelligence, Machine Learning, or related field. 7+ years of experience in AI/ML engineering, with a strong focus on LLM-based applications. At least 10+ years of experience in IT overall. Proven experience in building agent-based applications using Gemini, OpenAI or similar models. Deep understanding of RAG systems, vector databases, and knowledge retrieval strategies. Hands-on experience with LangChain and LangGraph frameworks. Solid background in model training, fine-tuning, evaluation and deployment. Strong coding skills in Python and experience with modern MLOps practices. What Could Set You Apart Familiarity with frontend integration of AI agents (Eg. using Angular, Mesop or similar frameworks). Experience with Google Cloud services like BigQuery, Vertex AI, Agent Builder. Exposure to Angular framework

Welcome to Warner Bros. Discovery… the stuff dreams are made of. Who We Are… When we say, “the stuff dreams are made of,” we’re not just referring to the world of wizards, dragons and superheroes, or even to the wonders of Planet Earth. Behind WBD’s vast portfolio of iconic content and beloved brands, are the storytellers bringing our characters to life, the creators bringing them to your living rooms and the dreamers creating what’s next… From brilliant creatives, to technology trailblazers, across the globe, WBD offers career defining opportunities, thoughtfully curated benefits, and the tools to explore and grow into your best selves. Here you are supported, here you are celebrated, here you can thrive. Your New Role : As Director – Analytics and Insights, you will architect and lead the future of data-driven decision-making at Warner Bros. Discovery. In this high-impact, global leadership role, you will own the strategy, delivery, and enterprise scaling of advanced analytics, business insights, and next-generation business intelligence (BI) platforms. You will be responsible for establishing a modern, intelligent insights ecosystem that empowers thousands of business users to make faster, smarter, and more strategic decisions—at scale. Your remit spans traditional analytics, enterprise BI (Tableau, Power BI, MicroStrategy, SAP BO), self-serve insights, conversational BI, and AI-augmented analytics, all under a single, unified vision. This role demands a visionary leader who can blend data architecture with business intuition, scale operational excellence, and drive measurable business value through insights. You will directly influence how WBD leverages data to optimize global operations, delight audiences, and accelerate innovation across its vast content and media ecosystem. 1. Enterprise Analytics & Insights Strategy Develop and execute a unified enterprise strategy for analytics and insights—anchored in business impact, speed-to-decision, and user empowerment. Define and lead strategic programs that embed analytics into content optimization, audience engagement, operational efficiency, marketing attribution, and revenue management. Transform the organization’s decision-making from reactive reporting to proactive, predictive, and prescriptive insights. 2. Business Intelligence Platform Leadership Own the global BI & reporting function, overseeing development, governance, performance, and innovation across platforms like Power BI, Tableau, MicroStrategy, and SAP BusinessObjects. Lead the modernization and consolidation of BI tools and workflows to drive consistency, scalability, and cost-efficiency. Establish enterprise-wide KPI frameworks, reporting templates, and executive dashboards to align operational and strategic decision-making. 3. AI-Enabled, Self-Service & Conversational BI Spearhead the implementation of AI-powered BI , including self-service analytics, conversational interfaces, and natural language querying. Drive adoption of augmented analytics capabilities—automated insights, anomaly detection, forecast narratives, and proactive alerts. Enable a federated, insight-driven culture by designing intuitive, role-based BI experiences for business users, creatives, and executives alike. 4. Cross-Functional Leadership & Stakeholder Engagement Act as a strategic advisor to senior leadership, translating complex analytics into actionable business insights with measurable outcomes. Partner closely with data engineering, data governance, data science, product, content, ad sales, marketing, and finance teams to ensure alignment and impact. Embed a data-first mindset across the enterprise through training, advocacy, and thought leadership. 5. People Leadership & Operational Excellence Lead, inspire, and grow a high-performing global team of analysts, BI developers, product owners, and insight consultants. Institutionalize delivery excellence by building reusable assets, scalable reporting templates, and high-impact insight playbooks. Monitor performance and adoption of analytics solutions and drive continuous improvement. 6. Innovation, Governance & Future Readiness Champion innovation in decision intelligence by exploring cutting-edge techniques such as causal inference, simulation modeling, and AI-generated narratives. Define and enforce BI governance, access controls, and data quality frameworks to maintain integrity, trust, and compliance. Stay at the forefront of analytics trends and tools, ensuring WBD’s analytics stack evolves ahead of the curve. Qualifications & Experiences: Master’s in Business Analytics, Data Science, Computer Science, Statistics, Economics, or related field. 10+ years of progressive experience in analytics, insights, and BI—ideally within media, entertainment, or direct-to-consumer businesses. At least 5+ years of proven leadership experience in managing large, global teams across analytics and BI disciplines. Deep expertise in enterprise BI platforms such as Power BI, Tableau, MicroStrategy, SAP BusinessObjects , and experience leading BI modernization programs. Proven success in deploying AI-enabled analytics , augmented BI , conversational interfaces , and self-serve data platforms at scale. Strong understanding of modern cloud data stacks (Snowflake, BigQuery), data modeling, SQL, and analytical architecture. Exceptional executive communication, data storytelling, and stakeholder management skills. A strategic thinker with a hands-on ability to connect analytics execution with tangible business results. How We Get Things Done… This last bit is probably the most important! Here at WBD, our guiding principles are the core values by which we operate and are central to how we get things done. You can find them at www.wbd.com/guiding-principles/ along with some insights from the team on what they mean and how they show up in their day to day. We hope they resonate with you and look forward to discussing them during your interview. Championing Inclusion at WBD Warner Bros. Discovery embraces the opportunity to build a workforce that reflects a wide array of perspectives, backgrounds and experiences. Being an equal opportunity employer means that we take seriously our responsibility to consider qualified candidates on the basis of merit, regardless of sex, gender identity, ethnicity, age, sexual orientation, religion or belief, marital status, pregnancy, parenthood, disability or any other category protected by law. If you’re a qualified candidate with a disability and you require adjustments or accommodations during the job application and/or recruitment process, please visit our accessibility page for instructions to submit your request.

Hyderabad, India Who we are Kinara is a Bay Area-based venture backed company. Our architecture is based on research done at Stanford University by Rehan Hameed and Wajahat Qadeer under the guidance of legendary Prof. Mark Horowitz (http://www-vlsi.stanford.edu/~horowitz/) and Prof. Christos Kozyrakis (http://csl.stanford.edu/~christos/). What we do Our game-changing AI solutions revolutionize what people and businesses can achieve. Ara inference processors combined with our SDK deliver unrivalled deep learning performance at the edge to accelerate and optimize real-time decision making where every millisecond is critical, and power efficiency is a must. Kinara solutions embed high- performance AI into edge devices to create a smarter, safer, and more enjoyable world. Edge AI is on the brink of a boom, and Kinara is looking forward to playing a significant role in it. Job Summary We are seeking an experienced developers for our kernel development team focused on building and optimizing AI/ML operators using our specialised Instruction Set Architecture (ISA). In this role, you will be responsible for design, development, and performance tuning of core kernel components that directly influence the efficiency and reliability of AI/ML workloads on our custom hardware. You will work closely with compiler teams, hardware architects, and application developers to deliver an operator that maximizes performance while meeting stringent accuracy and latency requirements. Key Responsibilities Design and development of core kernel modules, optimized for both performance and energy efficiency under AI/ML workloads. Design and development of advanced performance profiling/optimization and debugging tools to ensure low latency. Analyse kernel performance, identify bottlenecks, and implement optimizations at the software and feedback to the hardware for next generation improvements. Ensure to follow and propose industry best practices for code quality, performance testing, and validation of kernels. Collaborate with hardware architects, compiler teams to align the kernel design with the underlying ISA capabilities and seamless integration of AIML models. Stay current on emerging trends in ISA design, low-level programming, and AI/ML hardware acceleration. Ensure that documentation of the kernel components is made. Necessary Qualifications Bachelor’s or Master’s degree in Computer Science, Electrical/Computer Engineering, or a related field. Work experience in C/C++ is required and python is plus. 3+ years of industry experience in kernel or low-level system software development, with a strong background in optimizing performance for specialized hardware. Appropriate understanding of assembly language, computer architecture, ISA design/Development, and related performance optimization techniques. Demonstrated experience in using debugging and performance profiling tools (e.g., kernel debuggers, profilers, trace analyzers). Experience with developing AI/ML operators or accelerators in hardware-software co-design environments, is a plus. Experience of relevant industry standards and emerging trends in AI hardware acceleration is a plus. What We Offer An opportunity to lead innovative kernel development projects at the cutting-edge intersection of AI/ML and custom ISA design. A collaborative, dynamic work environment with access to state-of-the-art technology and methodologies. Competitive compensation, comprehensive benefits, and significant opportunities for professional growth and impact. Work culture We at Kinara have an environment that fosters innovation. Our team has technology experts who understand the big picture and mentors who coach passionate professionals to work on the most exciting challenges. We share responsibilities in everything we do, where every point of view is valued. Join us!