P-995 At Databricks, we are passionate about enabling data teams to solve the world's toughest problems — from making the next mode of transportation a reality to accelerating the development of medical breakthroughs. We do this by building and running the world's best data and AI infrastructure platform so our customers can use deep data insights to improve their business. Founded by engineers — and customer obsessed — we leap at every opportunity to solve technical challenges, from designing next-gen UI/UX for interfacing with data to scaling our services and infrastructure across millions of virtual machines. And we're only getting started. As one of the first Engineering Managers in the Software Engineering team at Databricks India , you will work with your team to build infrastructure and products for the Databricks platform at scale . We have multiple teams working on different domains. Resource management infrastructure powering the big data and machine learning workloads on the Databricks platform in a scalable, secure, and cloud-agnostic way Develop reliable, scalable services and client libraries that work with massive amounts of data on the cloud, across geographic regions and Cloud providers Build tools to allow Databricks engineers to operate their services across different clouds and environments Build services, products and infrastructure at the intersection of machine learning and distributed systems. The Impact You Will Have Hire great engineers to build an outstanding team. Ensure high technical standards by instituting processes (architecture reviews, testing) and culture (engineering excellence). Work with engineering and product leadership to build a long-term roadmap. Coordinate execution and collaborate across teams to unblock cross-cutting projects. What We Look For 10+ years of extensive experience with large-scale distributed systems alongside the processes around testing, monitoring, SLAs etc Extensive experience as a Software Engineering Leader , building & scaling software engineering teams from ground up Extensive experience managing a team of strong software engineers Partner with PM, Sales, and Customers to develop innovative features & products. BS (or higher) in Computer Science, or a related field. About Databricks Databricks is the data and AI company. More than 10,000 organizations worldwide — including Comcast, Condé Nast, Grammarly, and over 50% of the Fortune 500 — rely on the Databricks Data Intelligence Platform to unify and democratize data, analytics and AI. Databricks is headquartered in San Francisco, with offices around the globe and was founded by the original creators of Lakehouse, Apache Spark™, Delta Lake and MLflow. To learn more, follow Databricks on Twitter, LinkedIn and Facebook. Benefits At Databricks, we strive to provide comprehensive benefits and perks that meet the needs of all of our employees. For specific details on the benefits offered in your region, please visit https://www.mybenefitsnow.com/databricks. Our Commitment to Diversity and Inclusion At Databricks, we are committed to fostering a diverse and inclusive culture where everyone can excel. We take great care to ensure that our hiring practices are inclusive and meet equal employment opportunity standards. Individuals looking for employment at Databricks are considered without regard to age, color, disability, ethnicity, family or marital status, gender identity or expression, language, national origin, physical and mental ability, political affiliation, race, religion, sexual orientation, socio-economic status, veteran status, and other protected characteristics. Compliance If access to export-controlled technology or source code is required for performance of job duties, it is within Employer's discretion whether to apply for a U.S. government license for such positions, and Employer may decline to proceed with an applicant on this basis alone. Show more Show less

Job Summary As a Senior Data Scientist specializing in NLP, Generative AI, and Cloud technologies, you will be responsible for driving the development of data extraction solutions from documents at scale. This role requires advanced technical expertise in machine learning, NLP, and cloud computing, with a focus on automating document understanding processes and enhancing the quality of data extraction through state-of-the-art techniques. You will lead the design, implementation, and deployment of scalable NLP and AI models, mentor junior data scientists, and work collaboratively with cross-functional teams to deliver innovative solutions. This is a strategic role that requires both deep technical knowledge and leadership capabilities to shape the future of document data extraction within the organization. Key Responsibilities Lead Data Extraction Solutions: Design, implement, and scale advanced NLP and machine learning models for automating the extraction of structured data from a wide range of unstructured documents (e.g., PDFs, scanned images, contracts, reports, etc.). Generative AI Expertise: Leverage Generative AI models (such as GPT, BERT, and related architectures) for tasks such as document summarization, content generation, and enhancing extracted data. Cloud-Based Deployment: Architect and deploy data extraction models and workflows in cloud environments (AWS, Azure, GCP), ensuring scalability, reliability, and cost-efficiency. Model Development & Optimization: Develop and fine-tune machine learning and NLP models, ensuring high performance in accuracy, efficiency, and robustness for real-world data extraction tasks. Data Pipeline Design: Build and optimize end-to-end data pipelines, including data preprocessing, feature engineering, and model deployment, to process large-scale document datasets in the cloud. Cross-Functional Collaboration: Work closely with product, engineering, and business teams to understand requirements, provide technical solutions, and deliver impactful data-driven results. Research & Innovation: Stay up-to-date with the latest advancements in NLP, machine learning, and AI, applying cutting-edge research to improve data extraction methodologies. Mentorship & Leadership: Lead and mentor a team of junior data scientists, providing guidance on best practices, model development, and cloud deployment. Model Monitoring & Maintenance: Establish systems for monitoring model performance in production and ensure models are maintained and updated based on new data or changing requirements. Compliance & Security: Ensure data processing and extraction workflows adhere to industry standards, data privacy regulations, and security protocols, particularly when working with sensitive information. Required Skills & Qualifications Experience: Minimum 8 years of experience as a Data Scientist or similar role, with a focus on NLP, machine learning, and AI. At least 3 years in a senior or lead capacity. NLP & Document Processing Expertise: Proven experience applying NLP techniques such as Named Entity Recognition (NER), Optical Character Recognition (OCR), information extraction, document classification, and semantic analysis for data extraction from unstructured text. Generative AI: Advanced knowledge of Generative AI models (e.g., GPT-3, BERT, T5) and experience applying them to real-world document and text processing tasks. Cloud Technologies: Extensive experience with cloud platforms (AWS, Azure, or GCP) for deploying data pipelines, managing machine learning models, and processing large datasets. Programming Skills: Proficiency in Python and libraries such as SpaCy, Hugging Face Transformers, TensorFlow, PyTorch, and scikit-learn. Data Pipeline & DevOps Tools: Hands-on experience with building, optimizing, and deploying data pipelines in cloud environments, including tools like Docker, Kubernetes, Apache Airflow, and MLFlow. Data Handling & Analysis: Expertise in data manipulation and analysis using tools such as Pandas, NumPy, and SQL, and ability to work with large datasets. Leadership & Communication: Strong leadership and mentoring abilities, with excellent written and verbal communication skills to explain complex technical concepts to non-technical stakeholders. Problem Solving: Exceptional problem-solving skills with a creative approach to tackling challenges related to document data extraction. Collaboration: Experience working in a collaborative, cross-functional team environment to deliver end-to-end solutions. Preferred Qualifications Advanced Degree: Master’s or PhD in Computer Science, Data Science, Artificial Intelligence, or a related field. Advanced NLP Techniques: Experience with state-of-the-art NLP methods such as transfer learning, attention mechanisms, and reinforcement learning applied to document data extraction. Compliance Experience: Familiarity with legal, financial, or healthcare industry regulations regarding data privacy and document processing. Industry Experience: Previous experience in industries such as finance, legal, healthcare, or other sectors that heavily rely on document data extraction. Show more Show less

This role is for one of our clients Industry: Technology, Information and Media Seniority level: Associate level Min Experience: 2 years Location: India JobType: full-time About The Role We are looking for a proactive and skilled AWS Developer to join our dynamic team focused on cloud infrastructure and AI-driven solutions. In this role, you will architect, deploy, and maintain scalable and secure cloud environments on AWS, supporting the development and operationalization of machine learning models and AI applications. You will collaborate closely with data scientists, developers, and DevOps teams to ensure seamless integration and robust performance of AI workloads in the cloud. What You’ll Do Architect and build highly available, fault-tolerant, and scalable AWS infrastructure tailored for AI and machine learning workloads. Deploy, manage, and monitor AI/ML models in production using AWS services such as SageMaker, Lambda, EC2, ECS, and EKS. Partner with AI and ML teams to translate model requirements into effective cloud architectures and operational workflows. Automate infrastructure deployment and management through Infrastructure as Code (IaC) using Terraform, CloudFormation, or similar tools. Implement and optimize CI/CD pipelines to streamline model training, validation, and deployment processes. Monitor cloud environments and AI workloads proactively to identify and resolve performance bottlenecks or security vulnerabilities. Enforce best practices for data security, compliance, and governance in handling AI datasets and inference endpoints. Stay updated with AWS advancements and emerging tools to continuously enhance AI infrastructure capabilities. Support troubleshooting efforts, perform root cause analysis, and document solutions to maintain high system reliability. Who You Are 2+ years of hands-on experience working with AWS cloud services, especially in deploying and managing AI/ML workloads. Strong knowledge of AWS core services including S3, EC2, Lambda, SageMaker, IAM, CloudWatch, ECR, ECS, EKS, and CloudFormation. Experience deploying machine learning models into production environments and maintaining their lifecycle. Proficient in scripting and programming languages such as Python, Bash, or Node.js for automation and orchestration tasks. Skilled with containerization and orchestration tools such as Docker and Kubernetes (EKS). Familiar with monitoring and alerting solutions like AWS CloudWatch, Prometheus, or Grafana. Understanding of CI/CD methodologies and tools like Jenkins, GitHub Actions, or AWS CodePipeline. Bachelor’s degree in Computer Science, Engineering, or a related technical discipline. Bonus Points For AWS certifications such as AWS Certified Machine Learning – Specialty or AWS Solutions Architect. Hands-on experience with MLOps frameworks (Kubeflow, MLflow) and model version control. Familiarity with big data processing tools like Apache Spark, AWS Glue, or Redshift. Experience working in Agile or Scrum-based development environments. Show more Show less

We are seeking a skilled Lead Software Engineer to join our team and lead a project focused on developing GenAI applications using Large Language Models (LLMs) and Python programming . In this role, you will be responsible for designing and optimizing Al-generated text prompts to maximize effectiveness for various applications. You will also collaborate with cross-functional teams to ensure seamless integration of optimized prompts into the overall product or system. Your expertise in prompt engineering principles and techniques will allow you to guide models to desired outcomes and evaluate prompt performance to identify areas for optimization and iteration. Responsibilities Design, develop, test and refine AI-generated text prompts to maximize effectiveness for various applications Ensure seamless integration of optimized prompts into the overall product or system Rigorously evaluate prompt performance using metrics and user feedback Collaborate with cross-functional teams to understand requirements and ensure prompts align with business goals and user needs Document prompt engineering processes and outcomes, educate teams on prompt best practices and keep updated on the latest AI advancements to bring innovative solutions to the project Requirements 7 to 12 years of relevant professional experience Expertise in Python programming including experience with Al/machine learning frameworks like TensorFlow, PyTorch, Keras, Langchain, MLflow, Promtflow 2-5 years of working knowledge of NLP and LLMs like BERT, GPT-3/4, T5, etc. Knowledge of how these models work and how to fine-tune them Expertise in prompt engineering principles and techniques like chain of thought, in-context learning, tree of thought, etc. Knowledge of retrieval augmented generation (RAG) Strong analytical and problem-solving skills with the ability to think critically and troubleshoot issues Excellent communication skills, both verbal and written in English at a B2+ level for collaborating across teams, explaining technical concepts, and documenting work outcomes Show more Show less

Who We Are Zinnia is the leading technology platform for accelerating life and annuities growth. With innovative enterprise solutions and data insights, Zinnia simplifies the experience of buying, selling, and administering insurance products. All of which enables more people to protect their financial futures. Our success is driven by a commitment to three core values: be bold, team up, deliver value – and that we do. Zinnia has over $180 billion in assets under administration, serves 100+ carrier clients, 2500 distributors and partners, and over 2 million policyholders. Who You Are We are looking for a passionate and skilled Python, AI/ML Engineer with 5-7 years of experience to join our team. You will work on cutting-edge projects involving Generative AI, machine learning, and scalable systems, helping to build intelligent solutions that deliver real business value. If you thrive in a fast-paced environment and love solving complex problems using data and intelligent algorithms, we’d love to hear from you. What You’ll Do Design, develop, and deploy machine learning models and Generative AI solutions. Work on end-to-end ML pipelines, from data ingestion and preprocessing to model deployment and monitoring. Collaborate with cross-functional teams to understand requirements and deliver AI-driven features. Build robust, scalable, and well-documented Python-based APIs for ML services. Optimize database interactions and ensure efficient data storage and retrieval for AI applications. Stay updated with latest trends in AI/ML and integrate innovative approaches into projects. What You’ll Need Python – Strong hands-on experience. Machine Learning – Practical knowledge of supervised, unsupervised, and deep learning techniques. Generative AI – Experience working with LLMs or similar GenAI technologies. API Development – RESTful APIs and integration of ML models into production services. Databases – Experience with SQL and NoSQL databases (e.g., PostgreSQL, MongoDB, etc.). Good To Have Skills Cloud Platforms – Familiarity with AWS, Azure, or Google Cloud Platform (GCP). TypeScript/JavaScript – Frontend or full-stack exposure for ML product interfaces. Experience with MLOps tools and practices (e.g., MLflow, Kubeflow, etc.) Exposure to containerization (Docker) and orchestration (Kubernetes). WHAT’S IN IT FOR YOU? At Zinnia, you collaborate with smart, creative professionals who are dedicated to delivering cutting-edge technologies, deeper data insights, and enhanced services to transform how insurance is done. Visit our website at www.zinnia.com for more information. Apply by completing the online application on the careers section of our website. We are an Equal Opportunity employer committed to a diverse workforce. We do not discriminate based on race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability Show more Show less

As a global leader in cybersecurity, CrowdStrike protects the people, processes and technologies that drive modern organizations. Since 2011, our mission hasn’t changed — we’re here to stop breaches, and we’ve redefined modern security with the world’s most advanced AI-native platform. We work on large scale distributed systems, processing almost 3 trillion events per day. We have 3.44 PB of RAM deployed across our fleet of C* servers - and this traffic is growing daily. Our customers span all industries, and they count on CrowdStrike to keep their businesses running, their communities safe and their lives moving forward. We’re also a mission-driven company. We cultivate a culture that gives every CrowdStriker both the flexibility and autonomy to own their careers. We’re always looking to add talented CrowdStrikers to the team who have limitless passion, a relentless focus on innovation and a fanatical commitment to our customers, our community and each other. Ready to join a mission that matters? The future of cybersecurity starts with you. About The Role The charter of the Data + ML Platform team is to harness all the data that is ingested and cataloged within the Data LakeHouse for exploration, insights, model development, ML Engineering and Insights Activation. This team is situated within the larger Data Platform group, which serves as one of the core pillars of our company. We process data at a truly immense scale. Our processing is composed of various facets including threat events collected via telemetry data, associated metadata, along with IT asset information, contextual information about threat exposure based on additional processing, etc. These facets comprise the overall data platform, which is currently over 200 PB and maintained in a hyper scale Data Lakehouse, built and owned by the Data Platform team. The ingestion mechanisms include both batch and near real-time streams that form the core Threat Analytics Platform used for insights, threat hunting, incident investigations and more. As an engineer in this team, you will play an integral role as we build out our ML Experimentation Platform from the ground up. You will collaborate closely with Data Platform Software Engineers, Data Scientists & Threat Analysts to design, implement, and maintain scalable ML pipelines that will be used for Data Preparation, Cataloging, Feature Engineering, Model Training, and Model Serving that influence critical business decisions. You’ll be a key contributor in a production-focused culture that bridges the gap between model development and operational success. Future plans include generative AI investments for use cases such as modeling attack paths for IT assets. What You’ll Do Help design, build, and facilitate adoption of a modern Data+ML platform Modularize complex ML code into standardized and repeatable components Establish and facilitate adoption of repeatable patterns for model development, deployment, and monitoring Build a platform that scales to thousands of users and offers self-service capability to build ML experimentation pipelines Leverage workflow orchestration tools to deploy efficient and scalable execution of complex data and ML pipelines Review code changes from data scientists and champion software development best practices Leverage cloud services like Kubernetes, blob storage, and queues in our cloud first environment What You’ll Need B.S. in Computer Science, Data Science, Statistics, Applied Mathematics, or a related field and 7 + years related experience; or M.S. with 5+ years of experience; or Ph.D with 6+ years of experience. 3+ years experience developing and deploying machine learning solutions to production. Familiarity with typical machine learning algorithms from an engineering perspective (how they are built and used, not necessarily the theory); familiarity with supervised / unsupervised approaches: how, why, and when and labeled data is created and used 3+ years experience with ML Platform tools like Jupyter Notebooks, NVidia Workbench, MLFlow, Ray, Vertex AI etc. Experience building data platform product(s) or features with (one of) Apache Spark, Flink or comparable tools in GCP. Experience with Iceberg is highly desirable. Proficiency in distributed computing and orchestration technologies (Kubernetes, Airflow, etc.) Production experience with infrastructure-as-code tools such as Terraform, FluxCD Expert level experience with Python; Java/Scala exposure is recommended. Ability to write Python interfaces to provide standardized and simplified interfaces for data scientists to utilize internal Crowdstrike tools Expert level experience with CI/CD frameworks such as GitHub Actions Expert level experience with containerization frameworks Strong analytical and problem solving skills, capable of working in a dynamic environment Exceptional interpersonal and communication skills. Work with stakeholders across multiple teams and synthesize their needs into software interfaces and processes. Experience With The Following Is Desirable Go Iceberg Pinot or other time-series/OLAP-style database Jenkins Parquet Protocol Buffers/GRPC VJ1 Benefits Of Working At CrowdStrike Remote-friendly and flexible work culture Market leader in compensation and equity awards Comprehensive physical and mental wellness programs Competitive vacation and holidays for recharge Paid parental and adoption leaves Professional development opportunities for all employees regardless of level or role Employee Resource Groups, geographic neighbourhood groups and volunteer opportunities to build connections Vibrant office culture with world class amenities Great Place to Work Certified™ across the globe CrowdStrike is proud to be an equal opportunity employer. We are committed to fostering a culture of belonging where everyone is valued for who they are and empowered to succeed. We support veterans and individuals with disabilities through our affirmative action program. CrowdStrike is committed to providing equal employment opportunity for all employees and applicants for employment. The Company does not discriminate in employment opportunities or practices on the basis of race, color, creed, ethnicity, religion, sex (including pregnancy or pregnancy-related medical conditions), sexual orientation, gender identity, marital or family status, veteran status, age, national origin, ancestry, physical disability (including HIV and AIDS), mental disability, medical condition, genetic information, membership or activity in a local human rights commission, status with regard to public assistance, or any other characteristic protected by law. We base all employment decisions--including recruitment, selection, training, compensation, benefits, discipline, promotions, transfers, lay-offs, return from lay-off, terminations and social/recreational programs--on valid job requirements. If you need assistance accessing or reviewing the information on this website or need help submitting an application for employment or requesting an accommodation, please contact us at recruiting@crowdstrike.com for further assistance. Show more Show less

Overview: We are looking for a hands-on, full-cycle AI/ML Engineer who will play a central role in developing a cutting-edge AI agent platform. This platform is designed to automate and optimize complex workflows by leveraging large language models (LLMs), retrieval-augmented generation (RAG), knowledge graphs, and agent orchestration frameworks. As the AI/ML Engineer, you will be responsible for building intelligent agents from the ground up — including prompt design, retrieval pipelines, fine-tuning models, and deploying them in a secure, scalable cloud environment. You’ll also implement caching strategies, handle backend integration, and prototype user interfaces for internal and client testing. This role requires deep technical skills, autonomy, and a passion for bringing applied AI solutions into real-world use. Key Responsibilities: Design and implement modular AI agents using large language models (LLMs) to automate and optimize a variety of complex workflows Deploy and maintain end-to-end agent/AI workflows and services in cloud environments, ensuring reliability, scalability, and low-latency performance for production use Build and orchestrate multi-agent systems using frameworks like LangGraph or CrewAI, supporting context-aware, multi-step reasoning and task execution Develop and optimize retrieval-augmented generation (RAG) pipelines using vector databases (e.g., Qdrant, Pinecone, FAISS) to power semantic search and intelligent document workflows Fine-tune LLMs using frameworks such as Hugging Face Transformers, LoRA/PEFT, DeepSpeed, or Accelerate to create domain-adapted models Integrate knowledge graphs (e.g., Neo4j, AWS Neptune) into agent pipelines for context enhancement, reasoning, and relationship modeling Implement cache-augmented generation strategies using semantic caching and tools like Redis or vector similarity to reduce latency and improve consistency Build scalable backend services using FastAPI or Flask and develop lightweight user interfaces or prototypes with tools like Streamlit, Gradio, or React Monitor and evaluate model and agent performance using prompt testing, feedback loops, observability tools, and safe AI practices Collaborate with architects, product managers, and other developers to translate problem statements into scalable, reliable, and explainable AI systems Stay updated on the latest in cloud platforms (AWS/GCP/Azure), software frameworks, agentic frameworks, and AI/ML technologies Prerequisites: Strong Python development skills, including API development and service integration Experience with LLM APIs (OpenAI, Anthropic, Hugging Face), agent frameworks (LangChain, LangGraph, CrewAI), and prompt engineering Experience deploying AI-powered applications using Docker, cloud infrastructure (Azure preferred), and managing inference endpoints, vector DBs, and knowledge graph integrations in a live production setting Proven experience with RAG pipelines and vector databases (Qdrant, Pinecone, FAISS) Hands-on experience fine-tuning LLMs using PyTorch, Hugging Face Transformers, and optionally TensorFlow, with knowledge of LoRA, PEFT, or distributed training tools like DeepSpeed Familiarity with knowledge graphs and graph databases such as Neo4j or AWS Neptune, including schema design and Cypher/Gremlin querying Basic frontend prototyping skills using Streamlit or Gradio, and ability to work with frontend teams if needed Working knowledge of MLOps practices (e.g., MLflow, Weights & Biases), containerization (Docker), Git, and CI/CD workflows Cloud deployment experience with Azure, AWS, or GCP environments Understanding of caching strategies, embedding-based similarity, and response optimization through semantic caching Preferred Qualifications: Bachelor’s degree in Technology (B.Tech) or Master of Computer Applications (MCA) is required; MS in similar field preferred 7–10 years of experience in AI/ML, with at least 2 years focused on large language models, applied NLP, or agent-based systems Demonstrated ability to build and ship real-world AI-powered applications or platforms, preferably involving agents or LLM-centric workflows Strong analytical, problem-solving, and communication skills Ability to work independently in a fast-moving, collaborative, and cross-functional environment Prior experience in startups, innovation labs, or consulting firms a plus Compensation: The compensation structurewill be discussed during the interview Show more Show less

interface.ai is the industry's-leading specialized AI provider for banks and credit unions, serving over 100 financial institutions. The company's integrated AI platform offers a unified banking experience through voice, chat, and employee-assisting solutions, enhanced by cutting-edge proprietary Generative AI. Our mission is clear: to transform the banking experience so every consumer enjoys hyper-personalized, secure, and seamless interactions, while improving operational efficiencies and driving revenue growth. interface.ai offers pre-trained, domain-specific AI solutions that are easy to integrate, scale, and manage, both in-branch and online. Combining this with deep industry expertise, interface.ai is the AI solution for banks and credit unions that want to deliver exceptional experiences and stay at the forefront of AI innovation. About Interface.ai interface.ai is the most advanced AI platform for financial institutions. We serve over 100 credit unions and community banks, enabling millions of intelligent conversations every day through voice, chat, and internal copilots. As a fast-growing, AI-native company, data is at the heart of how we build, measure, and scale our products. From intelligent conversation design to customer automation analytics, we apply machine learning and statistical modeling to deliver real-time, measurable outcomes. About The Role We are seeking a Senior Data Scientist to lead the development of scalable, production-grade models and analytics systems that power core platform that our Products run on This is a high-impact role where you'll work on problems at the intersection of language understanding, user behavior prediction, decision optimization, and platform-level intelligence . You will be embedded in product-driven teams, while also collaborating with infrastructure and research to shape the future of intelligence at interface.ai. Key Responsibilities Develop and deploy machine learning models for use cases like intent recognition, conversation scoring, outcome prediction, and next-best-action systems Design and run A/B and multivariate experiments to validate hypotheses and measure product impact Build real-time and batch inference pipelines in collaboration with engineering Define, instrument, and maintain data pipelines for user interaction modeling, longitudinal engagement, and behavioral segmentation Develop intelligence layers for customer-facing analytics products (e.g., AI explainability, task attribution, feature impact modeling) Partner with product managers, engineers, and UX teams to define data-informed product features Translate complex model outcomes into actionable insights for internal and external stakeholders Stay current with research and best practices in Voice models, decision modeling, time-series analysis, and agentic AI architectures What Success Looks Like Within your first 6–12 months, you will: Launch production-grade models that are actively used in product features or operations workflows Define and validate key behavioral or predictive models that influence roadmap direction Improve accuracy, performance, or interpretability of existing AI systems across voice and chat products Drive measurable lift in engagement, resolution rate, or automation through data-driven product iterations Collaborate across departments to establish trusted experimentation and measurement frameworks Required What You Bring 5+ years of experience in applied data science, including end-to-end model development and deployment Strong knowledge of Python, R, SQL, and experience with ML libraries such and deep learning frameworks. Experience with statistical testing, experiment design, and causal inference Understanding of production ML pipelines and collaboration with data engineering teams Experience with speech models, conversational systems, or classification models in user-facing applications Strong product thinking—able to translate model insights into product impact and roadmap trade-offs Preferred Experience working in B2C environments especially in regulated industries (e.g., financial services, healthcare) Exposure to retrieval-augmented generation (RAG), embedding-based search, or LLM evaluation frameworks Familiarity with tools like Airflow, MLflow, dbt, or feature stores Prior work in chatbots, IVRs, or user feedback systems Why Join Us Data science is central to our product innovation strategy You’ll have a direct, measurable impact on customer outcomes and platform intelligence You’ll work on real-world AI applications with scaled deployment and product visibility You’ll collaborate with a cross-disciplinary team of engineers, designers, and product leaders moving at startup speed At interface.ai, we are committed to providing an inclusive and welcoming environment for all employees and applicants. We celebrate diversity and believe it is critical to our success as a company. We do not discriminate on the basis of race, color, religion, national origin, age, sex, gender identity, gender expression, sexual orientation, marital status, veteran status, disability status, or any other legally protected status. All employment decisions at Interface.ai are based on business needs, job requirements, and individual qualifications. We strive to create a culture that values and respects each person's unique perspective and contributions. We encourage all qualified individuals to apply for employment opportunities with Interface.ai and are committed to ensuring that our hiring process is inclusive and accessible. Show more Show less

Job Title: Data Scientist Location: Remote Job Type: Full-Time | Permanent Experience Required: 4+ Years About the Role: We are looking for a highly motivated and analytical Data Scientist with 4 years of industry experience to join our data team. The ideal candidate will have a strong background in Python , SQL , and experience deploying machine learning models using AWS SageMaker . You will be responsible for solving complex business problems with data-driven solutions, developing models, and helping scale machine learning systems into production environments. Key Responsibilities: Model Development: Design, develop, and validate machine learning models for classification, regression, and clustering tasks. Work with structured and unstructured data to extract actionable insights and drive business outcomes. Deployment & MLOps: Deploy machine learning models using AWS SageMaker , including model training, tuning, hosting, and monitoring. Build reusable pipelines for model deployment, automation, and performance tracking. Data Exploration & Feature Engineering: Perform data wrangling, preprocessing, and feature engineering using Python and SQL . Conduct EDA (exploratory data analysis) to identify patterns and anomalies. Collaboration: Work closely with data engineers, product managers, and business stakeholders to define data problems and deliver scalable solutions. Present model results and insights to both technical and non-technical audiences. Continuous Improvement: Stay updated on the latest advancements in machine learning, AI, and cloud technologies. Suggest and implement best practices for experimentation, model governance, and documentation. Required Skills & Qualifications: Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, or related field. 4+ years of hands-on experience in data science, machine learning, or applied AI roles. Proficiency in Python for data analysis, model development, and scripting. Strong SQL skills for querying and manipulating large datasets. Hands-on experience with AWS SageMaker , including model training, deployment, and monitoring. Solid understanding of machine learning algorithms and techniques (supervised/unsupervised). Familiarity with libraries such as Pandas, NumPy, Scikit-learn, Matplotlib, and Seaborn. Preferred Qualifications (Nice to Have): Experience with MLOps tools (e.g., MLflow, SageMaker Pipelines). Exposure to deep learning frameworks like TensorFlow or PyTorch. Knowledge of AWS data ecosystem (e.g., S3, Redshift, Athena). Experience in A/B testing or statistical experimentation Show more Show less

AIML Development_Full-Time, Permanent_Noida Job Title: AIML Development Job Type: Full-Time, Permanent Location: Noida Experience: 2-7 years Job Description: Roles: * AI/ML Developer (2-3 years experience) – Develop, fine-tune, and deploy AI/ML models. * Senior AI/ML Developer (5-7 years experience) – Lead AI/ML projects, optimize large-scale models, and mentor junior developers. What We’re Looking For: * B.Tech / M.Tech in Computer Science, AI, or related fields. * AI/ML Developer:2-3 years of experience in AI/ML development, strong in Python, TensorFlow/PyTorch, data engineering. * Senior AI/ML Developer:5-7 years of experience with expertise in LMs, deep learning, scalable architectures, and performance optimization. * Experience with MLOps tools MLflow, Kubeflow, Docker, Kubernetes) and cloud-based AI solutions. * Strong understanding of data structures, algorithms, and model deployment. * Be part of a team that’s shaping the future of AI-powered applications! Show more Show less

We’re hiring an Applied AI Researcher in the deep-tech and AI research industry . We are seeking a highly skilled and motivated professional to join our advanced research team. In this role, you will design multi-agent architectures, develop domain-specific scaffolding techniques, and build evaluation frameworks for next-generation AI systems. You’ll work at the frontier of applied AI—combining LLMs, reinforcement learning, and multi-agent systems to build scalable and meaningful solutions. Key Responsibilities Architect and implement novel multi-agent systems that enable advanced problem-solving and collaboration Design domain-specific scaffolding techniques to tailor AI behavior to complex, real-world domains Curate and manage high-quality datasets for training and evaluation of AI models across scientific and industrial use cases Establish and iterate on robust evaluation frameworks to measure performance, alignment, and robustness Research and apply reinforcement learning techniques, including RLHF, DPO, GRPO, and others Explore post-training optimization, fine-tuning, and domain adaptation methods Collaborate cross-functionally with engineering and product teams to translate research into production-ready solutions Stay abreast of cutting-edge developments in AI and contribute to internal and external research communities Document and communicate findings through technical reports, presentations, and publications Required Qualifications Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field 4+ years of experience in applied AI research or equivalent industry R&D experience Strong foundations in optimization, probability, and linear algebra Expertise in Python and frameworks like PyTorch or JAX Experience with RL and post-training methods (e.g., SFT, DPO, RLHF) Proficiency in building and aligning small language models (SLMs), including reasoning-specific models Familiarity with prompting strategies like Chain of Thought and dataset design for reasoning tasks Deep understanding of multi-agent systems and distributed training (multi-GPU/multi-node) Experience in designing evaluation metrics and performance analysis methodologies Preferred Experience Publications in leading ML conferences (NeurIPS, ICLR, ICML, AAMAS, etc.) Experience applying AI to scientific domains like drug discovery, chip design, or materials science Exposure to multimodal models and VLMs Experience with open-ended systems and emergent behavior in agent-based learning Background in computational science (chemistry, physics, EE, or applied math) Familiarity with MLOps, Kubernetes, Ray, Hydra, and MLflow Experience with domain adaptation, interpretability, and model optimization for deployment Contributions to open-source AI projects Expertise in building GPU-accelerated pipelines and optimizing inference at scale What We Offer Work on high-impact, frontier research with real-world applications Access to high-performance computing resources A collaborative, intellectually stimulating environment Autonomy to explore novel ideas aligned with our mission Competitive salary, benefits, and opportunities for growth Skills: Research,Design,Multi-agent Systems,Models Show more Show less

We’re hiring an Applied AI Researcher in the deep-tech and AI research industry . We are seeking a highly skilled and motivated professional to join our advanced research team. In this role, you will design multi-agent architectures, develop domain-specific scaffolding techniques, and build evaluation frameworks for next-generation AI systems. You’ll work at the frontier of applied AI—combining LLMs, reinforcement learning, and multi-agent systems to build scalable and meaningful solutions. Key Responsibilities Architect and implement novel multi-agent systems that enable advanced problem-solving and collaboration Design domain-specific scaffolding techniques to tailor AI behavior to complex, real-world domains Curate and manage high-quality datasets for training and evaluation of AI models across scientific and industrial use cases Establish and iterate on robust evaluation frameworks to measure performance, alignment, and robustness Research and apply reinforcement learning techniques, including RLHF, DPO, GRPO, and others Explore post-training optimization, fine-tuning, and domain adaptation methods Collaborate cross-functionally with engineering and product teams to translate research into production-ready solutions Stay abreast of cutting-edge developments in AI and contribute to internal and external research communities Document and communicate findings through technical reports, presentations, and publications Required Qualifications Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field 4+ years of experience in applied AI research or equivalent industry R&D experience Strong foundations in optimization, probability, and linear algebra Expertise in Python and frameworks like PyTorch or JAX Experience with RL and post-training methods (e.g., SFT, DPO, RLHF) Proficiency in building and aligning small language models (SLMs), including reasoning-specific models Familiarity with prompting strategies like Chain of Thought and dataset design for reasoning tasks Deep understanding of multi-agent systems and distributed training (multi-GPU/multi-node) Experience in designing evaluation metrics and performance analysis methodologies Preferred Experience Publications in leading ML conferences (NeurIPS, ICLR, ICML, AAMAS, etc.) Experience applying AI to scientific domains like drug discovery, chip design, or materials science Exposure to multimodal models and VLMs Experience with open-ended systems and emergent behavior in agent-based learning Background in computational science (chemistry, physics, EE, or applied math) Familiarity with MLOps, Kubernetes, Ray, Hydra, and MLflow Experience with domain adaptation, interpretability, and model optimization for deployment Contributions to open-source AI projects Expertise in building GPU-accelerated pipelines and optimizing inference at scale What We Offer Work on high-impact, frontier research with real-world applications Access to high-performance computing resources A collaborative, intellectually stimulating environment Autonomy to explore novel ideas aligned with our mission Competitive salary, benefits, and opportunities for growth Skills: Research,Design,Multi-agent Systems,Models Show more Show less

We’re hiring an Applied AI Researcher in the deep-tech and AI research industry . We are seeking a highly skilled and motivated professional to join our advanced research team. In this role, you will design multi-agent architectures, develop domain-specific scaffolding techniques, and build evaluation frameworks for next-generation AI systems. You’ll work at the frontier of applied AI—combining LLMs, reinforcement learning, and multi-agent systems to build scalable and meaningful solutions. Key Responsibilities Architect and implement novel multi-agent systems that enable advanced problem-solving and collaboration Design domain-specific scaffolding techniques to tailor AI behavior to complex, real-world domains Curate and manage high-quality datasets for training and evaluation of AI models across scientific and industrial use cases Establish and iterate on robust evaluation frameworks to measure performance, alignment, and robustness Research and apply reinforcement learning techniques, including RLHF, DPO, GRPO, and others Explore post-training optimization, fine-tuning, and domain adaptation methods Collaborate cross-functionally with engineering and product teams to translate research into production-ready solutions Stay abreast of cutting-edge developments in AI and contribute to internal and external research communities Document and communicate findings through technical reports, presentations, and publications Required Qualifications Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field 4+ years of experience in applied AI research or equivalent industry R&D experience Strong foundations in optimization, probability, and linear algebra Expertise in Python and frameworks like PyTorch or JAX Experience with RL and post-training methods (e.g., SFT, DPO, RLHF) Proficiency in building and aligning small language models (SLMs), including reasoning-specific models Familiarity with prompting strategies like Chain of Thought and dataset design for reasoning tasks Deep understanding of multi-agent systems and distributed training (multi-GPU/multi-node) Experience in designing evaluation metrics and performance analysis methodologies Preferred Experience Publications in leading ML conferences (NeurIPS, ICLR, ICML, AAMAS, etc.) Experience applying AI to scientific domains like drug discovery, chip design, or materials science Exposure to multimodal models and VLMs Experience with open-ended systems and emergent behavior in agent-based learning Background in computational science (chemistry, physics, EE, or applied math) Familiarity with MLOps, Kubernetes, Ray, Hydra, and MLflow Experience with domain adaptation, interpretability, and model optimization for deployment Contributions to open-source AI projects Expertise in building GPU-accelerated pipelines and optimizing inference at scale What We Offer Work on high-impact, frontier research with real-world applications Access to high-performance computing resources A collaborative, intellectually stimulating environment Autonomy to explore novel ideas aligned with our mission Competitive salary, benefits, and opportunities for growth Skills: Research,Design,Multi-agent Systems,Models Show more Show less

We’re hiring an Applied AI Researcher in the deep-tech and AI research industry . We are seeking a highly skilled and motivated professional to join our advanced research team. In this role, you will design multi-agent architectures, develop domain-specific scaffolding techniques, and build evaluation frameworks for next-generation AI systems. You’ll work at the frontier of applied AI—combining LLMs, reinforcement learning, and multi-agent systems to build scalable and meaningful solutions. Key Responsibilities Architect and implement novel multi-agent systems that enable advanced problem-solving and collaboration Design domain-specific scaffolding techniques to tailor AI behavior to complex, real-world domains Curate and manage high-quality datasets for training and evaluation of AI models across scientific and industrial use cases Establish and iterate on robust evaluation frameworks to measure performance, alignment, and robustness Research and apply reinforcement learning techniques, including RLHF, DPO, GRPO, and others Explore post-training optimization, fine-tuning, and domain adaptation methods Collaborate cross-functionally with engineering and product teams to translate research into production-ready solutions Stay abreast of cutting-edge developments in AI and contribute to internal and external research communities Document and communicate findings through technical reports, presentations, and publications Required Qualifications Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field 4+ years of experience in applied AI research or equivalent industry R&D experience Strong foundations in optimization, probability, and linear algebra Expertise in Python and frameworks like PyTorch or JAX Experience with RL and post-training methods (e.g., SFT, DPO, RLHF) Proficiency in building and aligning small language models (SLMs), including reasoning-specific models Familiarity with prompting strategies like Chain of Thought and dataset design for reasoning tasks Deep understanding of multi-agent systems and distributed training (multi-GPU/multi-node) Experience in designing evaluation metrics and performance analysis methodologies Preferred Experience Publications in leading ML conferences (NeurIPS, ICLR, ICML, AAMAS, etc.) Experience applying AI to scientific domains like drug discovery, chip design, or materials science Exposure to multimodal models and VLMs Experience with open-ended systems and emergent behavior in agent-based learning Background in computational science (chemistry, physics, EE, or applied math) Familiarity with MLOps, Kubernetes, Ray, Hydra, and MLflow Experience with domain adaptation, interpretability, and model optimization for deployment Contributions to open-source AI projects Expertise in building GPU-accelerated pipelines and optimizing inference at scale What We Offer Work on high-impact, frontier research with real-world applications Access to high-performance computing resources A collaborative, intellectually stimulating environment Autonomy to explore novel ideas aligned with our mission Competitive salary, benefits, and opportunities for growth Skills: Research,Design,Multi-agent Systems,Models Show more Show less

We’re hiring an Applied AI Researcher in the deep-tech and AI research industry . We are seeking a highly skilled and motivated professional to join our advanced research team. In this role, you will design multi-agent architectures, develop domain-specific scaffolding techniques, and build evaluation frameworks for next-generation AI systems. You’ll work at the frontier of applied AI—combining LLMs, reinforcement learning, and multi-agent systems to build scalable and meaningful solutions. Key Responsibilities Architect and implement novel multi-agent systems that enable advanced problem-solving and collaboration Design domain-specific scaffolding techniques to tailor AI behavior to complex, real-world domains Curate and manage high-quality datasets for training and evaluation of AI models across scientific and industrial use cases Establish and iterate on robust evaluation frameworks to measure performance, alignment, and robustness Research and apply reinforcement learning techniques, including RLHF, DPO, GRPO, and others Explore post-training optimization, fine-tuning, and domain adaptation methods Collaborate cross-functionally with engineering and product teams to translate research into production-ready solutions Stay abreast of cutting-edge developments in AI and contribute to internal and external research communities Document and communicate findings through technical reports, presentations, and publications Required Qualifications Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field 4+ years of experience in applied AI research or equivalent industry R&D experience Strong foundations in optimization, probability, and linear algebra Expertise in Python and frameworks like PyTorch or JAX Experience with RL and post-training methods (e.g., SFT, DPO, RLHF) Proficiency in building and aligning small language models (SLMs), including reasoning-specific models Familiarity with prompting strategies like Chain of Thought and dataset design for reasoning tasks Deep understanding of multi-agent systems and distributed training (multi-GPU/multi-node) Experience in designing evaluation metrics and performance analysis methodologies Preferred Experience Publications in leading ML conferences (NeurIPS, ICLR, ICML, AAMAS, etc.) Experience applying AI to scientific domains like drug discovery, chip design, or materials science Exposure to multimodal models and VLMs Experience with open-ended systems and emergent behavior in agent-based learning Background in computational science (chemistry, physics, EE, or applied math) Familiarity with MLOps, Kubernetes, Ray, Hydra, and MLflow Experience with domain adaptation, interpretability, and model optimization for deployment Contributions to open-source AI projects Expertise in building GPU-accelerated pipelines and optimizing inference at scale What We Offer Work on high-impact, frontier research with real-world applications Access to high-performance computing resources A collaborative, intellectually stimulating environment Autonomy to explore novel ideas aligned with our mission Competitive salary, benefits, and opportunities for growth Skills: Research,Design,Multi-agent Systems,Models Show more Show less

We’re hiring an Applied AI Researcher in the deep-tech and AI research industry . We are seeking a highly skilled and motivated professional to join our advanced research team. In this role, you will design multi-agent architectures, develop domain-specific scaffolding techniques, and build evaluation frameworks for next-generation AI systems. You’ll work at the frontier of applied AI—combining LLMs, reinforcement learning, and multi-agent systems to build scalable and meaningful solutions. Key Responsibilities Architect and implement novel multi-agent systems that enable advanced problem-solving and collaboration Design domain-specific scaffolding techniques to tailor AI behavior to complex, real-world domains Curate and manage high-quality datasets for training and evaluation of AI models across scientific and industrial use cases Establish and iterate on robust evaluation frameworks to measure performance, alignment, and robustness Research and apply reinforcement learning techniques, including RLHF, DPO, GRPO, and others Explore post-training optimization, fine-tuning, and domain adaptation methods Collaborate cross-functionally with engineering and product teams to translate research into production-ready solutions Stay abreast of cutting-edge developments in AI and contribute to internal and external research communities Document and communicate findings through technical reports, presentations, and publications Required Qualifications Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field 4+ years of experience in applied AI research or equivalent industry R&D experience Strong foundations in optimization, probability, and linear algebra Expertise in Python and frameworks like PyTorch or JAX Experience with RL and post-training methods (e.g., SFT, DPO, RLHF) Proficiency in building and aligning small language models (SLMs), including reasoning-specific models Familiarity with prompting strategies like Chain of Thought and dataset design for reasoning tasks Deep understanding of multi-agent systems and distributed training (multi-GPU/multi-node) Experience in designing evaluation metrics and performance analysis methodologies Preferred Experience Publications in leading ML conferences (NeurIPS, ICLR, ICML, AAMAS, etc.) Experience applying AI to scientific domains like drug discovery, chip design, or materials science Exposure to multimodal models and VLMs Experience with open-ended systems and emergent behavior in agent-based learning Background in computational science (chemistry, physics, EE, or applied math) Familiarity with MLOps, Kubernetes, Ray, Hydra, and MLflow Experience with domain adaptation, interpretability, and model optimization for deployment Contributions to open-source AI projects Expertise in building GPU-accelerated pipelines and optimizing inference at scale What We Offer Work on high-impact, frontier research with real-world applications Access to high-performance computing resources A collaborative, intellectually stimulating environment Autonomy to explore novel ideas aligned with our mission Competitive salary, benefits, and opportunities for growth Skills: Research,Design,Multi-agent Systems,Models Show more Show less

We’re hiring an Applied AI Researcher in the deep-tech and AI research industry . We are seeking a highly skilled and motivated professional to join our advanced research team. In this role, you will design multi-agent architectures, develop domain-specific scaffolding techniques, and build evaluation frameworks for next-generation AI systems. You’ll work at the frontier of applied AI—combining LLMs, reinforcement learning, and multi-agent systems to build scalable and meaningful solutions. Key Responsibilities Architect and implement novel multi-agent systems that enable advanced problem-solving and collaboration Design domain-specific scaffolding techniques to tailor AI behavior to complex, real-world domains Curate and manage high-quality datasets for training and evaluation of AI models across scientific and industrial use cases Establish and iterate on robust evaluation frameworks to measure performance, alignment, and robustness Research and apply reinforcement learning techniques, including RLHF, DPO, GRPO, and others Explore post-training optimization, fine-tuning, and domain adaptation methods Collaborate cross-functionally with engineering and product teams to translate research into production-ready solutions Stay abreast of cutting-edge developments in AI and contribute to internal and external research communities Document and communicate findings through technical reports, presentations, and publications Required Qualifications Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field 4+ years of experience in applied AI research or equivalent industry R&D experience Strong foundations in optimization, probability, and linear algebra Expertise in Python and frameworks like PyTorch or JAX Experience with RL and post-training methods (e.g., SFT, DPO, RLHF) Proficiency in building and aligning small language models (SLMs), including reasoning-specific models Familiarity with prompting strategies like Chain of Thought and dataset design for reasoning tasks Deep understanding of multi-agent systems and distributed training (multi-GPU/multi-node) Experience in designing evaluation metrics and performance analysis methodologies Preferred Experience Publications in leading ML conferences (NeurIPS, ICLR, ICML, AAMAS, etc.) Experience applying AI to scientific domains like drug discovery, chip design, or materials science Exposure to multimodal models and VLMs Experience with open-ended systems and emergent behavior in agent-based learning Background in computational science (chemistry, physics, EE, or applied math) Familiarity with MLOps, Kubernetes, Ray, Hydra, and MLflow Experience with domain adaptation, interpretability, and model optimization for deployment Contributions to open-source AI projects Expertise in building GPU-accelerated pipelines and optimizing inference at scale What We Offer Work on high-impact, frontier research with real-world applications Access to high-performance computing resources A collaborative, intellectually stimulating environment Autonomy to explore novel ideas aligned with our mission Competitive salary, benefits, and opportunities for growth Skills: Research,Design,Multi-agent Systems,Models Show more Show less

We’re hiring an Applied AI Researcher in the deep-tech and AI research industry . We are seeking a highly skilled and motivated professional to join our advanced research team. In this role, you will design multi-agent architectures, develop domain-specific scaffolding techniques, and build evaluation frameworks for next-generation AI systems. You’ll work at the frontier of applied AI—combining LLMs, reinforcement learning, and multi-agent systems to build scalable and meaningful solutions. Key Responsibilities Architect and implement novel multi-agent systems that enable advanced problem-solving and collaboration Design domain-specific scaffolding techniques to tailor AI behavior to complex, real-world domains Curate and manage high-quality datasets for training and evaluation of AI models across scientific and industrial use cases Establish and iterate on robust evaluation frameworks to measure performance, alignment, and robustness Research and apply reinforcement learning techniques, including RLHF, DPO, GRPO, and others Explore post-training optimization, fine-tuning, and domain adaptation methods Collaborate cross-functionally with engineering and product teams to translate research into production-ready solutions Stay abreast of cutting-edge developments in AI and contribute to internal and external research communities Document and communicate findings through technical reports, presentations, and publications Required Qualifications Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field 4+ years of experience in applied AI research or equivalent industry R&D experience Strong foundations in optimization, probability, and linear algebra Expertise in Python and frameworks like PyTorch or JAX Experience with RL and post-training methods (e.g., SFT, DPO, RLHF) Proficiency in building and aligning small language models (SLMs), including reasoning-specific models Familiarity with prompting strategies like Chain of Thought and dataset design for reasoning tasks Deep understanding of multi-agent systems and distributed training (multi-GPU/multi-node) Experience in designing evaluation metrics and performance analysis methodologies Preferred Experience Publications in leading ML conferences (NeurIPS, ICLR, ICML, AAMAS, etc.) Experience applying AI to scientific domains like drug discovery, chip design, or materials science Exposure to multimodal models and VLMs Experience with open-ended systems and emergent behavior in agent-based learning Background in computational science (chemistry, physics, EE, or applied math) Familiarity with MLOps, Kubernetes, Ray, Hydra, and MLflow Experience with domain adaptation, interpretability, and model optimization for deployment Contributions to open-source AI projects Expertise in building GPU-accelerated pipelines and optimizing inference at scale What We Offer Work on high-impact, frontier research with real-world applications Access to high-performance computing resources A collaborative, intellectually stimulating environment Autonomy to explore novel ideas aligned with our mission Competitive salary, benefits, and opportunities for growth Skills: Research,Design,Multi-agent Systems,Models Show more Show less

We’re hiring an Applied AI Researcher in the deep-tech and AI research industry . We are seeking a highly skilled and motivated professional to join our advanced research team. In this role, you will design multi-agent architectures, develop domain-specific scaffolding techniques, and build evaluation frameworks for next-generation AI systems. You’ll work at the frontier of applied AI—combining LLMs, reinforcement learning, and multi-agent systems to build scalable and meaningful solutions. Key Responsibilities Architect and implement novel multi-agent systems that enable advanced problem-solving and collaboration Design domain-specific scaffolding techniques to tailor AI behavior to complex, real-world domains Curate and manage high-quality datasets for training and evaluation of AI models across scientific and industrial use cases Establish and iterate on robust evaluation frameworks to measure performance, alignment, and robustness Research and apply reinforcement learning techniques, including RLHF, DPO, GRPO, and others Explore post-training optimization, fine-tuning, and domain adaptation methods Collaborate cross-functionally with engineering and product teams to translate research into production-ready solutions Stay abreast of cutting-edge developments in AI and contribute to internal and external research communities Document and communicate findings through technical reports, presentations, and publications Required Qualifications Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field 4+ years of experience in applied AI research or equivalent industry R&D experience Strong foundations in optimization, probability, and linear algebra Expertise in Python and frameworks like PyTorch or JAX Experience with RL and post-training methods (e.g., SFT, DPO, RLHF) Proficiency in building and aligning small language models (SLMs), including reasoning-specific models Familiarity with prompting strategies like Chain of Thought and dataset design for reasoning tasks Deep understanding of multi-agent systems and distributed training (multi-GPU/multi-node) Experience in designing evaluation metrics and performance analysis methodologies Preferred Experience Publications in leading ML conferences (NeurIPS, ICLR, ICML, AAMAS, etc.) Experience applying AI to scientific domains like drug discovery, chip design, or materials science Exposure to multimodal models and VLMs Experience with open-ended systems and emergent behavior in agent-based learning Background in computational science (chemistry, physics, EE, or applied math) Familiarity with MLOps, Kubernetes, Ray, Hydra, and MLflow Experience with domain adaptation, interpretability, and model optimization for deployment Contributions to open-source AI projects Expertise in building GPU-accelerated pipelines and optimizing inference at scale What We Offer Work on high-impact, frontier research with real-world applications Access to high-performance computing resources A collaborative, intellectually stimulating environment Autonomy to explore novel ideas aligned with our mission Competitive salary, benefits, and opportunities for growth Skills: Research,Design,Multi-agent Systems,Models Show more Show less

We’re hiring an Applied AI Researcher in the deep-tech and AI research industry . We are seeking a highly skilled and motivated professional to join our advanced research team. In this role, you will design multi-agent architectures, develop domain-specific scaffolding techniques, and build evaluation frameworks for next-generation AI systems. You’ll work at the frontier of applied AI—combining LLMs, reinforcement learning, and multi-agent systems to build scalable and meaningful solutions. Key Responsibilities Architect and implement novel multi-agent systems that enable advanced problem-solving and collaboration Design domain-specific scaffolding techniques to tailor AI behavior to complex, real-world domains Curate and manage high-quality datasets for training and evaluation of AI models across scientific and industrial use cases Establish and iterate on robust evaluation frameworks to measure performance, alignment, and robustness Research and apply reinforcement learning techniques, including RLHF, DPO, GRPO, and others Explore post-training optimization, fine-tuning, and domain adaptation methods Collaborate cross-functionally with engineering and product teams to translate research into production-ready solutions Stay abreast of cutting-edge developments in AI and contribute to internal and external research communities Document and communicate findings through technical reports, presentations, and publications Required Qualifications Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field 4+ years of experience in applied AI research or equivalent industry R&D experience Strong foundations in optimization, probability, and linear algebra Expertise in Python and frameworks like PyTorch or JAX Experience with RL and post-training methods (e.g., SFT, DPO, RLHF) Proficiency in building and aligning small language models (SLMs), including reasoning-specific models Familiarity with prompting strategies like Chain of Thought and dataset design for reasoning tasks Deep understanding of multi-agent systems and distributed training (multi-GPU/multi-node) Experience in designing evaluation metrics and performance analysis methodologies Preferred Experience Publications in leading ML conferences (NeurIPS, ICLR, ICML, AAMAS, etc.) Experience applying AI to scientific domains like drug discovery, chip design, or materials science Exposure to multimodal models and VLMs Experience with open-ended systems and emergent behavior in agent-based learning Background in computational science (chemistry, physics, EE, or applied math) Familiarity with MLOps, Kubernetes, Ray, Hydra, and MLflow Experience with domain adaptation, interpretability, and model optimization for deployment Contributions to open-source AI projects Expertise in building GPU-accelerated pipelines and optimizing inference at scale What We Offer Work on high-impact, frontier research with real-world applications Access to high-performance computing resources A collaborative, intellectually stimulating environment Autonomy to explore novel ideas aligned with our mission Competitive salary, benefits, and opportunities for growth Skills: Research,Design,Multi-agent Systems,Models Show more Show less

Role Description Job Title: Lead Gen AI Architect Experience: 9–12 Years Location: PAN India (UST Offices) Mode of Work: Hybrid (3 days in office per week) Job Type: Full-Time Job Summary We are seeking a Lead Gen AI Architect to lead the design, development, and deployment of advanced AI and Generative AI solutions tailored to the real estate domain . This role combines deep technical expertise with strong architectural leadership and client-facing capabilities. You will collaborate closely with cross-functional teams and US-based clients, with a work schedule that includes overlap up to 12 AM IST . This is a hybrid role , requiring 3 days a week in-office at any UST location in India . Key Responsibilities Architect and implement end-to-end Generative AI solutions, preferably using OpenAI technologies. Design and build advanced NLP pipelines, including prompt engineering, Retrieval-Augmented Generation (RAG), and model fine-tuning. Develop and deploy AI Agents to support business automation and dynamic user interaction. Engage with internal teams and customers to gather requirements, present solutions, and ensure high-quality delivery. Guide and mentor development teams in writing scalable, maintainable Python code following best Machine Learning practices. Lead MLOps initiatives for efficient deployment and management of AI models. Collaborate with US-based teams, maintaining client interaction and delivery alignment during late hours (up to 12 AM IST). Mandatory Skills NLP & Generative AI Experience integrating GPT models (e.g., ChatGPT, GPT-4) Strong knowledge of Prompt Engineering Expertise in Retrieval-Augmented Generation (RAG) pipelines Model fine-tuning and performance optimization Experience building and deploying AI Agents using OpenAI or similar frameworks Programming Strong proficiency in Python Working knowledge of R (preferred) Machine Learning Hands-on experience with model development, training, validation, and optimization Experience deploying and scaling ML models in production environments Good-to-Have Skills API Development Experience with Dataiku Building and consuming RESTful APIs for AI model integration MLOps & Deployment Familiarity with MLflow, Docker, Kubernetes Data Engineering Proficiency in SQL, PySpark for data preparation and pipeline creation Data Visualization Experience with tools like Tableau or Power BI for stakeholder reporting Qualifications Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field 9–12 years of hands-on experience in AI/ML/Gen AI, with leadership roles in solution delivery Excellent verbal and written communication skills; confident in client-facing scenarios Experience or exposure to the real estate domain is an added advantage Why Join Us Lead innovation at the intersection of AI and real estate Work in a collaborative, impact-driven team environment Contribute to the development of scalable, real-world AI applications Enjoy a flexible and supportive hybrid work culture Skills Machine Learning,Natural Language Processing,GenAI,SQL Show more Show less

We’re hiring an Applied AI Researcher in the deep-tech and AI research industry . We are seeking a highly skilled and motivated professional to join our advanced research team. In this role, you will design multi-agent architectures, develop domain-specific scaffolding techniques, and build evaluation frameworks for next-generation AI systems. You’ll work at the frontier of applied AI—combining LLMs, reinforcement learning, and multi-agent systems to build scalable and meaningful solutions. Key Responsibilities Architect and implement novel multi-agent systems that enable advanced problem-solving and collaboration Design domain-specific scaffolding techniques to tailor AI behavior to complex, real-world domains Curate and manage high-quality datasets for training and evaluation of AI models across scientific and industrial use cases Establish and iterate on robust evaluation frameworks to measure performance, alignment, and robustness Research and apply reinforcement learning techniques, including RLHF, DPO, GRPO, and others Explore post-training optimization, fine-tuning, and domain adaptation methods Collaborate cross-functionally with engineering and product teams to translate research into production-ready solutions Stay abreast of cutting-edge developments in AI and contribute to internal and external research communities Document and communicate findings through technical reports, presentations, and publications Required Qualifications Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field 4+ years of experience in applied AI research or equivalent industry R&D experience Strong foundations in optimization, probability, and linear algebra Expertise in Python and frameworks like PyTorch or JAX Experience with RL and post-training methods (e.g., SFT, DPO, RLHF) Proficiency in building and aligning small language models (SLMs), including reasoning-specific models Familiarity with prompting strategies like Chain of Thought and dataset design for reasoning tasks Deep understanding of multi-agent systems and distributed training (multi-GPU/multi-node) Experience in designing evaluation metrics and performance analysis methodologies Preferred Experience Publications in leading ML conferences (NeurIPS, ICLR, ICML, AAMAS, etc.) Experience applying AI to scientific domains like drug discovery, chip design, or materials science Exposure to multimodal models and VLMs Experience with open-ended systems and emergent behavior in agent-based learning Background in computational science (chemistry, physics, EE, or applied math) Familiarity with MLOps, Kubernetes, Ray, Hydra, and MLflow Experience with domain adaptation, interpretability, and model optimization for deployment Contributions to open-source AI projects Expertise in building GPU-accelerated pipelines and optimizing inference at scale What We Offer Work on high-impact, frontier research with real-world applications Access to high-performance computing resources A collaborative, intellectually stimulating environment Autonomy to explore novel ideas aligned with our mission Competitive salary, benefits, and opportunities for growth Skills: Research,Design,Multi-agent Systems,Models Show more Show less

We’re hiring an Applied AI Researcher in the deep-tech and AI research industry . We are seeking a highly skilled and motivated professional to join our advanced research team. In this role, you will design multi-agent architectures, develop domain-specific scaffolding techniques, and build evaluation frameworks for next-generation AI systems. You’ll work at the frontier of applied AI—combining LLMs, reinforcement learning, and multi-agent systems to build scalable and meaningful solutions. Key Responsibilities Architect and implement novel multi-agent systems that enable advanced problem-solving and collaboration Design domain-specific scaffolding techniques to tailor AI behavior to complex, real-world domains Curate and manage high-quality datasets for training and evaluation of AI models across scientific and industrial use cases Establish and iterate on robust evaluation frameworks to measure performance, alignment, and robustness Research and apply reinforcement learning techniques, including RLHF, DPO, GRPO, and others Explore post-training optimization, fine-tuning, and domain adaptation methods Collaborate cross-functionally with engineering and product teams to translate research into production-ready solutions Stay abreast of cutting-edge developments in AI and contribute to internal and external research communities Document and communicate findings through technical reports, presentations, and publications Required Qualifications Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field 4+ years of experience in applied AI research or equivalent industry R&D experience Strong foundations in optimization, probability, and linear algebra Expertise in Python and frameworks like PyTorch or JAX Experience with RL and post-training methods (e.g., SFT, DPO, RLHF) Proficiency in building and aligning small language models (SLMs), including reasoning-specific models Familiarity with prompting strategies like Chain of Thought and dataset design for reasoning tasks Deep understanding of multi-agent systems and distributed training (multi-GPU/multi-node) Experience in designing evaluation metrics and performance analysis methodologies Preferred Experience Publications in leading ML conferences (NeurIPS, ICLR, ICML, AAMAS, etc.) Experience applying AI to scientific domains like drug discovery, chip design, or materials science Exposure to multimodal models and VLMs Experience with open-ended systems and emergent behavior in agent-based learning Background in computational science (chemistry, physics, EE, or applied math) Familiarity with MLOps, Kubernetes, Ray, Hydra, and MLflow Experience with domain adaptation, interpretability, and model optimization for deployment Contributions to open-source AI projects Expertise in building GPU-accelerated pipelines and optimizing inference at scale What We Offer Work on high-impact, frontier research with real-world applications Access to high-performance computing resources A collaborative, intellectually stimulating environment Autonomy to explore novel ideas aligned with our mission Competitive salary, benefits, and opportunities for growth Skills: Research,Design,Multi-agent Systems,Models Show more Show less

About Company Our client is a trusted global innovator of IT and business services. We help clients transform through consulting, industry solutions, business process services, digital & IT modernization and managed services. Our client enables them, as well as society, to move confidently into the digital future. We are committed to our clients’ long-term success and combine global reach with local client attention to serve them in over 50 countries around the globe Job Title: Senior AI Cloud Operations Engineer Location: Chennai Experience: 4 to 5 yrs Job Type : Contract to hire Notice Period:- Immediate joiner OffShore Profile Summary: We’re looking for a Senior AI Cloud Operations Engineer to start building a new for AI Cloud Operations team, starting with this strategic position. We are searching for an experienced Senior AI Cloud Operations Engineer with deep expertise in AI technologies to lead our cloud-based AI infrastructure management. This role is integral to ensuring our AI systems' scalability, reliability, and performance, enabling us to deliver cutting-edge solutions. The ideal candidate will have a robust understanding of machine learning frameworks, cloud services architecture, and operations management. Key Responsibilities: Cloud Architecture Design: Design, architect, and manage scalable cloud infrastructure tailored for AI workloads, leveraging platforms like AWS, Azure, or Google Cloud. System Monitoring and Optimization: Implement comprehensive monitoring solutions to ensure high availability and swift performance, utilizing tools like Prometheus, Grafana, or CloudWatch. Collaboration and Model Deployment: Work closely with data scientists to operationalize AI models, ensuring seamless integration with existing systems and workflows. Familiarity with tools such as MLflow or TensorFlow Serving can be beneficial. Automation and Orchestration: Develop automated deployment pipelines using orchestration tools like Kubernetes and Terraform to streamline operations and reduce manual interventions. Security and Compliance: Ensure that all cloud operations adhere to security best practices and compliance standards, including data privacy regulations like GDPR or HIPAA. Documentation and Reporting: Create and maintain detailed documentation of cloud configurations, procedures, and operational metrics to foster transparency and continuous improvement. Performance Tuning: Conduct regular performance assessments and implement strategies to optimize cloud resource utilization and reduce costs without compromising system effectiveness. Issue Resolution: Rapidly identify, diagnose, and resolve technical issues, minimizing downtime and ensuring maximum uptime. Qualifications: Educational Background: Bachelor’s degree in Computer Science, Engineering, or a related field. Master's degree preferred. Professional Experience: 5+ years of extensive experience in cloud operations, particularly within AI environments. Demonstrated expertise in deploying and managing complex AI systems in cloud settings. Technical Expertise: Deep knowledge of cloud platforms (AWS, Azure, Google Cloud) including their AI-specific services such as AWS SageMaker or Google AI Platform. AI/ML Proficiency: In-depth understanding of AI/ML frameworks and libraries such as TensorFlow, PyTorch, Scikit-learn, along with experience in ML model lifecycle management. Infrastructure as Code: Proficiency in infrastructure-as-code tools such as Terraform and AWS CloudFormation to automate and manage cloud deployment processes. Containerization and Microservices: Expertise in managing containerized applications using Docker and orchestrating services with Kubernetes. Soft Skills: Strong analytical, problem-solving, and communication skills, with the ability to work effectively both independently and in collaboration with cross-functional teams. Preferred Qualifications: Advanced certifications in cloud services, such as AWS Certified Solutions Architect or Google Cloud Professional Data Engineer. Experience in advanced AI techniques such as deep learning or reinforcement learning. Knowledge of emerging AI technologies and trends to drive innovation within existing infrastructure. List of Used Tools: Cloud Provider: Azure, AWS or Google. Performance & monitor: Prometheus, Grafana, or CloudWatch. Collaboration and Model Deployment: MLflow or TensorFlow Serving Automation and Orchestration: Kubernetes and Terraform Security and Compliance: Data privacy regulations like GDPR or HIPAA. Qualifications Bachelor's degree in Computer Science (or related field) Show more Show less

We’re hiring an Applied AI Researcher in the deep-tech and AI research industry . We are seeking a highly skilled and motivated professional to join our advanced research team. In this role, you will design multi-agent architectures, develop domain-specific scaffolding techniques, and build evaluation frameworks for next-generation AI systems. You’ll work at the frontier of applied AI—combining LLMs, reinforcement learning, and multi-agent systems to build scalable and meaningful solutions. Key Responsibilities Architect and implement novel multi-agent systems that enable advanced problem-solving and collaboration Design domain-specific scaffolding techniques to tailor AI behavior to complex, real-world domains Curate and manage high-quality datasets for training and evaluation of AI models across scientific and industrial use cases Establish and iterate on robust evaluation frameworks to measure performance, alignment, and robustness Research and apply reinforcement learning techniques, including RLHF, DPO, GRPO, and others Explore post-training optimization, fine-tuning, and domain adaptation methods Collaborate cross-functionally with engineering and product teams to translate research into production-ready solutions Stay abreast of cutting-edge developments in AI and contribute to internal and external research communities Document and communicate findings through technical reports, presentations, and publications Required Qualifications Master’s or PhD in Computer Science, Artificial Intelligence, Machine Learning, or a related field 4+ years of experience in applied AI research or equivalent industry R&D experience Strong foundations in optimization, probability, and linear algebra Expertise in Python and frameworks like PyTorch or JAX Experience with RL and post-training methods (e.g., SFT, DPO, RLHF) Proficiency in building and aligning small language models (SLMs), including reasoning-specific models Familiarity with prompting strategies like Chain of Thought and dataset design for reasoning tasks Deep understanding of multi-agent systems and distributed training (multi-GPU/multi-node) Experience in designing evaluation metrics and performance analysis methodologies Preferred Experience Publications in leading ML conferences (NeurIPS, ICLR, ICML, AAMAS, etc.) Experience applying AI to scientific domains like drug discovery, chip design, or materials science Exposure to multimodal models and VLMs Experience with open-ended systems and emergent behavior in agent-based learning Background in computational science (chemistry, physics, EE, or applied math) Familiarity with MLOps, Kubernetes, Ray, Hydra, and MLflow Experience with domain adaptation, interpretability, and model optimization for deployment Contributions to open-source AI projects Expertise in building GPU-accelerated pipelines and optimizing inference at scale What We Offer Work on high-impact, frontier research with real-world applications Access to high-performance computing resources A collaborative, intellectually stimulating environment Autonomy to explore novel ideas aligned with our mission Competitive salary, benefits, and opportunities for growth Skills: Research,Design,Multi-agent Systems,Models Show more Show less