169 Kubeflow Jobs - Page 7

In this role, you will drive the end-to-end development and deployment of AI/ML solutions on Cloud, applying your expertise in machine learning, MLOps, and GenAI. You will mentor junior engineers by establishing best practices and guiding solution design. Additionally, you will design, implement, and optimize advanced machine learning algorithms using robust data science techniques, while driving the scaling and adoption of AI/ML technologies to meet strategic business objectives. If you have a passion for AI, cloud-based ML solutions, and driving scalable innovation, this role is for you. You have: Bachelors or masters degree in computer science, Engineering, or related field with 8+ years of experience in machine learning, data science, and statistics. Expertise in ML algorithms with strong Python and SQL skills, including Jupyter notebooks. Proven knowledge of GenAI and Agentic AI technologies. Hands-on experience with MLOps tools like Kubeflow, Vertex AI, and GenAI/LLMops. Grip on, Kubernetes, Docker, and microservices architecture. It would be nice if you also had: Experience with OpenAI, Llama, and Lang Chain frameworks. Practical experience in Pytest and Rust programming. Prior experience developing AI applications in the telecom domain. Google Cloud Professional Machine Learning Engineer certification. Architect and develop AI/ML use cases on Cloud, driving projects from discovery and proof of concept through industrialization, deployment, and ongoing maintenance. Mentor and guide junior engineers in defining robust methodologies and evaluating the most effective technical solutions. Propose, implement, test, and select optimal machine learning algorithms by leveraging advanced statistical tools and data science techniques to uncover insights and generate accurate predictions. Drive the rapid scaling and integration of AI and ML technologies to enhance system performance and support strategic business objectives.

Roles and Responsibilities Design, Develop, and Deploy: Create and implement advanced machine learning models and algorithms to tackle complex business challenges across various domains, including Recommender Systems, Search, Computer Vision, Supply Chain Management (SCM), Pricing, Forecasting, Trend and Virality Prediction, Generative AI, and more. Technical Expertise: Demonstrate deep theoretical knowledge and practical expertise in one or more areas, such as Natural Language Processing (NLP), Computer Vision, Recommender Systems, Gen AI and Optimization. Software Development: Develop robust, scalable, and maintainable software solutions for seamless model deployment. CI/CD Pipelines: Set up and manage Continuous Integration/Continuous Deployment (CI/CD) pipelines for automated testing, deployment, and model integration. Model Maintenance: Maintain and optimize machine learning pipelines, including data cleaning, feature extraction, and model training. Collaboration: Work closely with the Platforms and Engineering teams to ensure smooth deployment and integration of ML models into production systems. Data Management: Partner with the Data Platforms team to gather, process, and analyze data crucial for model development. Code Quality: Write clean, efficient, and maintainable code following best practices. Performance Optimization: Conduct performance testing, troubleshooting, and tuning to ensure optimal model performance. Continuous Learning: Stay up-to-date with the latest advancements in machine learning and technology, sharing insights and knowledge across the organization. Qualifications & Experience Industry Experience: 3-5+ years with a Bachelor's degree, or 2+ years with a Masters/Ph.D. in Computer Science, Mathematics, Statistics, or a related field. Machine Learning Expertise: At least 2 years of hands-on experience as a Machine Learning Engineer or a similar role. Production Deployment: Proven experience deploying machine learning solutions into production environments. Programming Skills: Strong proficiency in Python or equivalent programming languages for model development. ML Frameworks: Familiarity with leading machine learning frameworks (Keras, TensorFlow, PyTorch) and libraries (scikit-learn). CI/CD Tools: Experience with CI/CD tools and practices. Communication: Excellent verbal and written communication skills. Teamwork & Independence: Ability to work collaboratively in a team environment or independently as needed. Workload Management: Strong organizational skills to manage and prioritize tasks, supporting your manager effectively. Exceptional candidates are encouraged to apply, even if you don't meet every listed qualification. We're open to hiring individuals who demonstrate outstanding potential. Nice to Have Research Contributions: Publications or presentations in recognized Machine Learning and Data Science journals/conferences. Big Data Technologies: Experience with big data technologies like Spark or other distributed computing frameworks. Cloud Services: Proficiency in cloud platforms (AWS, Google Cloud) and an understanding of distributed systems. Generative AI Exposure: Familiarity with Generative AI models. Database Management: Experience with SQL and/or NoSQL databases. ML Orchestration: Knowledge of ML orchestration tools (Airflow, Kubeflow, MLFlow).

Job Overview As a Machine Learning Engineer, you will oversee the entire lifecycle of machine learning models. Your role involves collaborating with cross-functional teams, including data scientists, data engineers, software engineers, and DevOps specialists, to bridge the gap between experimental model development and reliable production systems. You will be responsible for automating ML pipelines, optimizing model training and serving, ensuring model governance, and maintaining the stability of deployed systems. This position requires a blend of experience in software engineering, data engineering, and machine learning systems, along with a strong understanding of DevOps practices to enable faster experimentation, consistent performance, and scalable ML operations. What You Will Do Work with Data Science Leadership and Stakeholders to understand business objectives, map scope of work, and support colleagues in achieving technical deliverables. Invest in strong relationships with colleagues and build a successful followership around a common goal. Build and optimize ML pipelines for feature engineering, model training, and inference. Develop low-latency, high-throughput model endpoints for distributed environments. Maintain cloud infrastructure for ML workloads, including GPUs/TPUs, across platforms like GCP, AWS, or Azure Troubleshoot, debug, and validate ML systems for performance and reliability. Write and maintain automated tests (unit and integration). Supports discussions with Data Engineers to work on data collection, storage, and retrieval processes. Collaborate with Data Governance to identify data issues and propose data cleansing or enhancement solutions. Drive continuous improvement efforts in enhancing performance and providing increased functionality, including developing processes for automation. Skills You Will Need Group Work Lead: Ability to lead portions of pod iteratives; can clearly communicate priorities and play an effective technical support role for colleagues. Communication: Maintaining timely communication with management and stakeholders on project progress, issues, and concerns. Developing effective communication plans tailored to diverse audiences. Consultive Mindset: Go beyond just providing analytics and actively engage stakeholders to understand their challenges and goals. Ability to have a business-first viewpoint when developing solutions. Cloud & ML Ops: Expertise in managing cloud-based ML infrastructures (GCP, AWS, or Azure), coupled with DevOps practices, ensures seamless model deployment, scalability, and system reliability. This includes containerization, CI/CD pipelines, and infrastructure-as-code tools. Proficiency in programming languages such as Python, SQL, and Java. Who You Are 5+ years of industry experience working with machine learning tools and technologies. Familiarity with agile development frameworks and collaboration tools (e.g., JIRA, Confluence). Experience using Tensorflow, PyTorch, scikit-learn, Kubeflow, pandas and numpy. and frameworks like Ray, Dask preferred. Expertise in data engineering, object-oriented programming, and familiarity with microservices and cloud technologies. An ongoing learner who seeks out emerging technology and can influence others to think innovatively. Gets energized by fast-paced environments and capable of supporting multiple projects - can identify primary and secondary objectives, prioritize time, and communicate timelines to team members. Dedicated to fulfilling ideals of diversity, inclusion, and respect that company aspires to achieve every day in every way. Regularly required to sit, talk, hear; use hands/fingers to touch, handle, and feel. Occasionally required to move about the workplace and reach with hands and arms. Requires close vision. Able to work a flexible schedule based on department and company needs.

Position: Senior AI Architect AI Factory (MLOps, GenOps)Experience:20+ years of total IT experience with a minimum of 10 years in AI/ML Proven experience in building scalable AI platforms or "AI Factories" for productionizing machine learning and generative AI workflows, including strong hands-on expertise in MLOps and emerging GenOps practices Location:Bangalore / Pune on case-to-case basisRole Summary:We are looking for a Senior AI Architect to lead the design and implementation of a next-generation AI Factory platform that streamlines the development, deployment, monitoring, and reuse of AI/ML and GenAI assets This role will be instrumental in establishing scalable MLOps and GenOps practices, building reusable components, standardizing pipelines, and enabling cross-industry solutioning for pre-sales and delivery The candidate will work closely with the AI Practice Head, contributing to both business enablement and technical strategy while supporting customer engagements, RFP/RFI responses, PoCs, and accelerator development Key Responsibilities: Architect and build the AI Factory a central repository of reusable AI/ML models, GenAI prompts, agents, pipelines, APIs, and accelerators Define and implement MLOps workflows for versioning, model training, deployment, CI/CD, monitoring, and governance Design and integrate GenOps pipelines for prompt engineering, LLM orchestration, evaluation, and optimization Create blueprints and templates for standardized AI solution delivery across cloud platforms (Azure, AWS, GCP) Build accelerators and reusable modules to speed up AI solutioning for common use cases (e g , chatbots, summarization, document intelligence) Enable pre-sales and solution teams with reusable assets for demos, PoCs, and customer presentations Contribute to RFP/RFI responses with scalable, production-ready AI factory strategies and architectural documentation Collaborate with data engineering, DevOps, cloud, and security teams to ensure robust and enterprise-compliant AI solution delivery Required Skills: Deep experience in MLOps tools like MLflow, Kubeflow, SageMaker Pipelines, Azure ML Pipelines, or Vertex AI Pipelines Understanding of GenOps frameworks including prompt flow management, LLM evaluation (e g , TruLens, Ragas), and orchestration (LangChain, LlamaIndex, Semantic Kernel) Strong command of Python, YAML/JSON, and API integration for scalable AI component development Experience with CI/CD pipelines (GitHub Actions, Jenkins, Azure DevOps), containerization (Docker, Kubernetes), and model registries Familiar with model observability, drift detection, automated retraining, and model versioning Ability to create clean, reusable architecture artifacts and professional PowerPoint decks for customer and internal presentations Preferred Qualifications: Experience in building and managing an enterprise-wide AI marketplace or model catalog Familiarity with LLMOps platforms (eg, Weights & Biases, PromptLayer, Arize AI) Exposure to multi-cloud GenAI architectures and hybrid deployment models Cloud certifications in AI/ML from any major provider (AWS, Azure, GCP) Soft Skills: Strong leadership and mentoring capabilities Effective communication and storytelling skills for technical and non-technical audiences Innovation mindset with a passion for automation and efficiency Comfortable working in a fast-paced, cross-functional environment with shifting priorities

Walk-in interview only -BLR No virtual Python & libraries scikit-learn, TensorFlow,PyTorch/XGBoost exp in supervised, unsupervised, and deep learning techniques Deployment tools Flask, FastAPI, Docker AWS GCP, Azure &ML,SQL Contact Maya 9880516218

Position Title: AI/ML Engineer Company: Cyfuture India Pvt. Ltd. Industry: IT Services and IT Consulting Location: Sector 81, NSEZ, Noida (5 Days Work From Office) Website: www.cyfuture.com About Cyfuture Cyfuture is a trusted name in IT services and cloud infrastructure, offering state-of-the-art data center solutions and managed services across platforms like AWS, Azure, and VMWare. We are expanding rapidly in system integration and managed services, building strong alliances with global OEMs like VMWare, AWS, Azure, HP, Dell, Lenovo, and Palo Alto. Position Overview We are hiring an experienced AI/ML Engineer to lead and shape our AI/ML initiatives. The ideal candidate will have hands-on experience in machine learning and artificial intelligence, with strong leadership capabilities and a passion for delivering production-ready solutions. This role involves end-to-end ownership of AI/ML projects, from strategy development to deployment and optimization of large-scale systems. Key Responsibilities Lead and mentor a high-performing AI/ML team. Design and execute AI/ML strategies aligned with business goals. Collaborate with product and engineering teams to identify impactful AI opportunities. Build, train, fine-tune, and deploy ML models in production environments. Manage operations of LLMs and other AI models using modern cloud and MLOps tools. Implement scalable and automated ML pipelines (e.g., with Kubeflow or MLRun). Handle containerization and orchestration using Docker and Kubernetes. Optimize GPU/TPU resources for training and inference tasks. Develop efficient RAG pipelines with low latency and high retrieval accuracy. Automate CI/CD workflows for continuous integration and delivery of ML systems. Key Skills & Expertise 1. Cloud Computing & Deployment Proficiency in AWS, Google Cloud, or Azure for scalable model deployment. Familiarity with cloud-native services like AWS SageMaker, Google Vertex AI, or Azure ML. Expertise in Docker and Kubernetes for containerized deployments Experience with Infrastructure as Code (IaC) using tools like Terraform or CloudFormation. 2. Machine Learning & Deep Learning Strong command of frameworks: TensorFlow, PyTorch, Scikit-learn, XGBoost. Experience with MLOps tools for integration, monitoring, and automation. Expertise in pre-trained models, transfer learning, and designing custom architectures. 3. Programming & Software Engineering Strong skills in Python (NumPy, Pandas, Matplotlib, SciPy) for ML development. Backend/API development with FastAPI , Flask , or Django . Database handling with SQL and NoSQL (PostgreSQL, MongoDB, BigQuery). Familiarity with CI/CD pipelines (GitHub Actions, Jenkins). 4. Scalable AI Systems Proven ability to build AI-driven applications at scale. Handle large datasets, high-throughput requests, and real-time inference. Knowledge of distributed computing: Apache Spark, Dask, Ray . 5. Model Monitoring & Optimization Hands-on with model compression, quantization, and pruning . A/B testing and performance tracking in production. Knowledge of model retraining pipelines for continuous learning. 6. Resource Optimization Efficient use of compute resources: GPUs, TPUs, CPUs . Experience with serverless architectures to reduce cost. Auto-scaling and load balancing for high-traffic systems. 7. Problem-Solving & Collaboration Translate complex ML models into user-friendly applications. Work effectively with data scientists, engineers, and product teams. Write clear technical documentation and architecture reports . Udisha Parashar Senior Talent Acquisition Specialist Mob: +91- 9301895707 Email: udisha.parashar@cyfuture.com URL: www.cyfuture.com

AI/ML Engineer Work on cutting-edge technologies like Natural Language Processing (NLP), Generative AI, and Large Language Models (LLMs) Assist in building intelligent systems for real-world applications Collaborate with data scientists and product teams on end-to-end ML workflows Cloud DevOps Engineer Get hands-on with AWS, Azure, or GCP cloud platforms Automate infrastructure, CI/CD pipelines, monitoring, and deployments Learn Infrastructure as Code (IaC) using tools like Terraform or Cloud Formation Software Engineer (Backend/Frontend) Build scalable applications using Python, Node.js for backend and React.js for frontend Contribute to the full-stack development lifecycle Participate in design, coding, testing, and debugging of software components MLOps Engineer Automate and scale ML workflows from development to production Work with tools like MLflow, Kubeflow, Docker, Kubernetes Ensure reproducibility, monitoring, and performance of ML models in production Data Engineer Develop robust ETL pipelines, data ingestion, and transformation logic Work with databases, data warehouses, and data lakes Handle real-time and batch data workflows using tools like Apache Airflow, Spark, Kafka Eligibility : BE/B.Tech/MCA/MSc 2023 or 2024 pass outs

Senior DevOps Engineer Experience: 5 - 9 Years Exp Salary : Competitive Preferred Notice Period : Within 30 Days Shift : 10:00AM to 7:00PM IST Opportunity Type: Onsite (Ahmedabad) Placement Type: Permanent (*Note: This is a requirement for one of Uplers' Clients) Must have skills required : Azure (Microsoft Azure), Docker/Terraform, TensorFlow, Python, AWS Good to have skills : Kubeflow, MLFlow Attri (One of Uplers' Clients) is Looking for: Senior DevOps Engineer who is passionate about their work, eager to learn and grow, and who is committed to delivering exceptional results. If you are a team player, with a positive attitude and a desire to make a difference, then we want to hear from you. Role Overview Description About Attri Attri is an AI organization that helps businesses initiate and accelerate their AI efforts. We offer the industrys first end-to-end enterprise machine learning platform, empowering teams to focus on ML development rather than infrastructure. From ideation to execution, our global team of AI experts supports organizations in building scalable, state-of-the-art ML solutions. Our mission is to redefine businesses by harnessing cutting-edge technology and a unique, value-driven approach. With team members across continents, we celebrate diversity, curiosity, and innovation. Were now looking for a Senior DevOps Engineer to join our fast-growing, remote-first team. If you're passionate about automation, scalable cloud systems, and supporting high-impact AI workloads, wed love to connect. What You'll Do (Responsibilities): Design, implement, and manage scalable, secure, and high-performance cloud-native infrastructure across Azure. Build and maintain Infrastructure as Code (IaC) using Terraform or CloudFormation. Develop event-driven and serverless architectures using AWS Lambda, SQS, and SAM. Architect and manage containerized applications using Docker, Kubernetes, ECR, ECS, or AKS. Establish and optimize CI/CD pipelines using GitHub Actions, Jenkins, AWS CodeBuild & CodePipeline. Set up and manage monitoring, logging, and alerting using Prometheus + Grafana, Datadog, and centralized logging systems. Collaborate with ML Engineers and Data Engineers to support MLOps pipelines (Airflow, ML Pipelines) and Bedrock with Tensorflow or PyTorch. Implement and optimize ETL/data streaming pipelines using Kafka, EventBridge, and Event Hubs. Automate operations and system tasks using Python and Bash, along with Cloud CLIs and SDKs. Secure infrastructure using IAM/RBAC and follow best practices in secrets management and access control. Manage DNS and networking configurations using Cloudflare, VPC, and PrivateLink. Lead architecture implementation for scalable and secure systems, aligning with business and AI solution needs. Conduct cost optimization through budgeting, alerts, tagging, right-sizing resources, and leveraging spot instances. Contribute to backend development in Python (Web Frameworks), REST/Socket and gRPC design, and testing (unit/integration). Participate in incident response, performance tuning, and continuous system improvement. Good to Have: Hands-on experience with ML lifecycle tools like MLflow and Kubeflow Previous involvement in production-grade AI/ML projects or data-intensive systems Startup or high-growth tech company experience Qualifications: Bachelors degree in Computer Science, Information Technology, or a related field. 5+ years of hands-on experience in a DevOps, SRE, or Cloud Infrastructure role. Proven expertise in multi-cloud environments (AWS, Azure, GCP) and modern DevOps tooling. Strong communication and collaboration skills to work across engineering, data science, and product teams. Benefits: Competitive Salary Support for continual learning (free books and online courses) Leveling Up Opportunities Diverse team environment How to apply for this opportunity: Easy 3-Step Process: 1. Click On Apply! And Register or log in on our portal 2. Upload updated Resume & Complete the Screening Form 3. Increase your chances to get shortlisted & meet the client for the Interview! About Our Client: Attri, an AI organization, leads the way in enterprise AI, offering advanced solutions and services driven by AI agents and powered by Foundation Models. Our comprehensive suite of AI-enabled tools drives business impact, enhances quality, mitigates risk, and also helps unlock growth opportunities. About Uplers: Our goal is to make hiring and getting hired reliable, simple, and fast. Our role will be to help all our talents find and apply for relevant product and engineering job opportunities and progress in their career. (Note: There are many more opportunities apart from this on the portal.) So, if you are ready for a new challenge, a great work environment, and an opportunity to take your career to the next level, don't hesitate to apply today. We are waiting for you!

Ready to build the future with AI At Genpact, we don&rsquot just keep up with technology&mdashwe set the pace. AI and digital innovation are redefining industries, and we&rsquore leading the charge. Genpact&rsquos AI Gigafactory, our industry-first accelerator, is an example of how we&rsquore scaling advanced technology solutions to help global enterprises work smarter, grow faster, and transform at scale. From large-scale models to agentic AI, our breakthrough solutions tackle companies most complex challenges. If you thrive in a fast-moving, innovation-driven environment, love building and deploying cutting-edge AI solutions, and want to push the boundaries of what&rsquos possible, this is your moment. Genpact (NYSE: G) is an advanced technology services and solutions company that delivers lasting value for leading enterprises globally. Through our deep business knowledge, operational excellence, and cutting-edge solutions - we help companies across industries get ahead and stay ahead. Powered by curiosity, courage, and innovation, our teams implement data, technology, and AI to create tomorrow, today. Get to know us at genpact.com and on LinkedIn, X, YouTube, and Facebook. We invite applications for the role of Principal Consultant - Data Science Architect Job Description: We are seeking a highly experienced and visionary Data Science Architect to lead the design and implementation of scalable data science solutions across our organization. This role requires a strategic thinker with deep technical expertise in machine learning, data engineering, and analytics architecture. You will collaborate with cross-functional teams to drive innovation, optimize data workflows, and ensure the successful deployment of advanced analytics and AI solutions. Key Responsibilities: . Architect and implement end-to-end data science solutions, from data ingestion to model deployment and monitoring. . Collaborate with engineering, product, and business teams to define AI/ML strategies aligned with business goals. . Lead the selection and integration of data science tools, platforms, and frameworks. . Collaborate with data engineers, analysts, and business stakeholders to translate business problems into analytical solutions. . Define best practices for model governance, versioning, reproducibility, and scalability. . Mentor and guide data scientists and engineers on technical and architectural decisions. . Ensure data quality, security, and compliance across all data science initiatives. . Evaluate emerging technologies and trends in AI/ML to keep the organization at the forefront of innovation. Qualifications we seek in you! Minimum Qualifications . Bachelor%27s or Master&rsquos degree in Computer Science, Data Science, Statistics, or a related field (Ph.D. preferred). . experience in data science, with at least 2 years in an architect or lead role . Proven experience designing and deploying machine learning models in production environments. . Strong programming skills in Python, R, or Scala and experience with ML libraries (e.g., TensorFlow, PyTorch, Scikit-learn). . Expertise with with GenAI, LLMs, or advanced NLP techniques. Experience in building GenAI or LLM-based applications. . Familiarity with big data technologies (e.g., Spark, Kafka, Delta Lake) and modern data stack components . Experience in product engineering . Certifications in cloud architecture or data science. . Good understanding of cloud platforms (AWS, Azure, GCP) and MLOps tools (MLflow, Kubeflow, etc.). . Excellent communication skills and the ability to influence technical and non-technical stakeholders. Why join Genpact . Lead AI-first transformation - Build and scale AI solutions that redefine industries . Make an impact - Drive change for global enterprises and solve business challenges that matter . Accelerate your career&mdashGain hands-on experience, world-class training, mentorship, and AI certifications to advance your skills . Grow with the best - Learn from top engineers, data scientists, and AI experts in a dynamic, fast-moving workplace . Committed to ethical AI - Work in an environment where governance, transparency, and security are at the core of everything we build . Thrive in a values-driven culture - Our courage, curiosity, and incisiveness - built on a foundation of integrity and inclusion - allow your ideas to fuel progress Come join the 140,000+ coders, tech shapers, and growth makers at Genpact and take your career in the only direction that matters: Up. Let&rsquos build tomorrow together. Genpact is an Equal Opportunity Employer and considers applicants for all positions without regard to race, color, religion or belief, sex, age, national origin, citizenship status, marital status, military/veteran status, genetic information, sexual orientation, gender identity, physical or mental disability or any other characteristic protected by applicable laws. Genpact is committed to creating a dynamic work environment that values respect and integrity, customer focus, and innovation. Furthermore, please do note that Genpact does not charge fees to process job applications and applicants are not required to pay to participate in our hiring process in any other way. Examples of such scams include purchasing a %27starter kit,%27 paying to apply, or purchasing equipment or training.

Senior Technical Architect Machine Learning Solutions We are looking for a Senior Technical Architect with deep expertise in Machine Learning (ML), Artificial Intelligence (AI) , and scalable ML system design . This role will focus on leading the end-to-end architecture of advanced ML-driven platforms, delivering impactful, production-grade AI solutions across the enterprise. Key Responsibilities Lead the architecture and design of enterprise-grade ML platforms , including data pipelines, model training pipelines, model inference services, and monitoring frameworks. Architect and optimize ML lifecycle management systems (MLOps) to support scalable, reproducible, and secure deployment of ML models in production. Design and implement retrieval-augmented generation (RAG) systems, vector databases , semantic search , and LLM orchestration frameworks (e.g., LangChain, Autogen). Define and enforce best practices in model development, versioning, CI/CD pipelines , model drift detection, retraining, and rollback mechanisms. Build robust pipelines for data ingestion, preprocessing, feature engineering , and model training at scale , using batch and real-time streaming architectures. Architect multi-modal ML solutions involving NLP, computer vision, time-series, or structured data use cases. Collaborate with data scientists, ML engineers, DevOps, and product teams to convert research prototypes into scalable production services . Implement observability for ML models including custom metrics, performance monitoring, and explainability (XAI) tooling. Evaluate and integrate third-party LLMs (e.g., OpenAI, Claude, Cohere) or open-source models (e.g., LLaMA, Mistral) as part of intelligent application design. Create architectural blueprints and reference implementations for LLM APIs, model hosting, fine-tuning, and embedding pipelines . Guide the selection of compute frameworks (GPUs, TPUs), model serving frameworks (e.g., TorchServe, Triton, BentoML) , and scalable inference strategies (batch, real-time, streaming). Drive AI governance and responsible AI practices including auditability, compliance, bias mitigation, and data protection. Stay up to date on the latest developments in ML frameworks, foundation models, model compression, distillation, and efficient inference . 14. Ability to coach and lead technical teams , fostering growth, knowledge sharing, and technical excellence in AI/ML domains. Experience managing the technical roadmap for AI-powered products , documentations ensuring timely delivery, performance optimization, and stakeholder alignment. Required Qualifications Bachelors or Master’s degree in Computer Science, Artificial Intelligence, Data Science, or a related field. 8+ years of experience in software architecture , with 5+ years focused specifically on machine learning systems and 2 years in leading team. Proven expertise in designing and deploying ML systems at scale , across cloud and hybrid environments. Strong hands-on experience with ML frameworks (e.g., PyTorch, TensorFlow, Hugging Face, Scikit-learn). Experience with vector databases (e.g., FAISS, Pinecone, Weaviate, Qdrant) and embedding models (e.g., SBERT, OpenAI, Cohere). Demonstrated proficiency in MLOps tools and platforms : MLflow, Kubeflow, SageMaker, Vertex AI, DataBricks, Airflow, etc. In-depth knowledge of cloud AI/ML services on AWS, Azure, or GCP – including certification(s) in one or more platforms. Experience with containerization and orchestration (Docker, Kubernetes) for model packaging and deployment. Ability to design LLM-based systems , including hybrid models (open-source + proprietary), fine-tuning strategies, and prompt engineering. Solid understanding of security, compliance , and AI risk management in ML deployments. Preferred Skills Experience with AutoML , hyperparameter tuning, model selection, and experiment tracking. Knowledge of LLM tuning techniques : LoRA, PEFT, quantization, distillation, and RLHF. Knowledge of privacy-preserving ML techniques , federated learning, and homomorphic encryption Familiarity with zero-shot, few-shot learning , and retrieval-enhanced inference pipelines. Contributions to open-source ML tools or libraries. Experience deploying AI copilots, agents, or assistants using orchestration frameworks.

Lead the development of scalable and efficient machine learning platform products. Develop technical specifications and architectures for new ML platform features and products. Collaborate with cross-functional teams across the globe to ensure product development aligns with business goals. Define and enforce engineering best practices to ensure high-quality deliverables. Participate in the code review process to ensure our code quality standards are met. Stay up-to-date with the latest ML platform technologies and trends to identify opportunities for product innovation. Participate in the hiring and onboarding process for new team members Communicate effectively with technical and non-technical stakeholders to ensure alignment on project goals and timelines. Required Skills and Experience - Candidate should have a Bachelors or Masters in Computer Science or equivalent Demonstrate Technical leadership with hands-on coding experience. 9+ years of experience of commercial software development with at least 3+ years working within the Machine Learning / AI space, Demonstrated excellence participating on cross functional teams in fast-paced environments, both in terms of technical leadership and hands-on coding. Excellent ability to break down complex problems into simple solutions. Willingness and ability to learn, evaluate, and make recommendations for leveraging new technologies. Strong analytical skills and desire to write clean, correct and efficient code. Sense of ownership, urgency and pride in your work. Proven that you are a leader who prioritizes, communicates clearly, and partners effectively with both technical and nontechnical employees. Excellent command of tools and expertise for troubleshooting production issues. Experience working with Python, Docker, Kubernetes. Experience with Kubeflow, Seldon, Spark and AWS / Azure cloud service experience is a plus.

Youll make a difference by: Siemens is seeking a visionary and technically strong Lead AI/ML Engineer to spearhead the development of intelligent systems that power the future of sustainable and connected transportation. This role will lead the design and deployment of AI/ML solutions across domains such as efficiency improvements in software development process, predictive maintenance, traffic analytics, computer vision for rail safety, and intelligent automation in rolling stock and rail infrastructure. Key Responsibilities Lead the end-to-end lifecycle of AI/ML projectsfrom data acquisition and model development to deployment and monitoringwithin the context of mobility systems. Architect scalable ML pipelines that integrate with Siemens Mobility platforms and other edge/cloud-based systems. Collaborate with multi-functional teams including domain experts, software architects, and system engineers to translate mobility use cases into AI-driven solutions. Mentor junior engineers and data scientists, and foster a culture of innovation, quality, and continuous improvement. Evaluate and integrate innovative research in AI/ML, including generative AI, computer vision, and time-series forecasting, into real-world applications. Ensure compliance with Siemens AI ethics, cybersecurity, and data governance standards. Required Qualifications Bachelor's or Masters or PhD in Computer Science, Machine Learning, Data Science, or a related field. 7+ years of experience in AI/ML engineering, with at least 2 years in a technical leadership role. Strong programming skills in Python and experience with ML frameworks such as TensorFlow, PyTorch, and Scikit-learn. Proven experience deploying ML models in production, preferably in industrial or mobility environments. Familiarity with MLOps tools (e.g., MLflow, Kubeflow) and cloud platforms (Azure, AWS, or GCP). Solid understanding of data engineering, model versioning, and CI/CD for ML. Preferred Qualifications Experience in transportation, automotive, or industrial automation domains. Knowledge of edge AI deployment, sensor fusion, or real-time analytics. Contributions to open-source AI/ML projects or published research. What We Offer Opportunity to shape the future of mobility through AI innovation. Access to Siemens global network of experts, labs, and digital platforms, flexible work arrangements, and continuous learning opportunities. A mission-driven environment focused on sustainability, safety, and digital transformation. Desired Skills: 9+ years of experience is required. Great Communication skills. Analytical and problem-solving skills

Job Overview (Primary Skills - GCP, Kubeflow, Python, Vertex.ai) As a Machine Learning Engineer, you will oversee the entire lifecycle of machine learning models. Your role involves collaborating with cross-functional teams, including data scientists, data engineers, software engineers, and DevOps specialists, to bridge the gap between experimental model development and reliable production systems. You will be responsible for automating ML pipelines, optimizing model training and serving, ensuring model governance, and maintaining the stability of deployed systems. This position requires a blend of experience in software engineering, data engineering, and machine learning systems, along with a strong understanding of DevOps practices to enable faster experimentation, consistent performance, and scalable ML operations. What You Will Do Work with data science leadership and stakeholders to understand business objectives, map the scope of work, and support colleagues in achieving technical deliverables. Invest in strong relationships with colleagues and build a successful followership around a common goal. Build and optimize ML pipelines for feature engineering, model training, and inference. Develop low-latency, high-throughput model endpoints for distributed environments. Maintain cloud infrastructure for ML workloads, including GPUs/TPUs, across platforms like GCP, AWS, or Azure Troubleshoot, debug, and validate ML systems for performance and reliability. Write and maintain automated tests (unit and integration). Supports discussions with Data Engineers to work on data collection, storage, and retrieval processes. Collaborate with Data Governance to identify data issues and propose data cleansing or enhancement solutions. Drive continuous improvement efforts in enhancing performance and providing increased functionality, including developing processes for automation. Skills You Will Need Group Work Lead: Ability to lead portions of pod iteratives; can clearly communicate priorities and play an effective technical support role for colleagues. Communication: Maintaining timely communication with management and stakeholders on project progress, issues, and concerns. Developing effective communication plans tailored to diverse audiences. Consultive Mindset: Go beyond just providing analytics and actively engage stakeholders to understand their challenges and goals. Ability to have a business-first viewpoint when developing solutions. Cloud & ML Ops: Expertise in managing cloud-based ML infrastructures (GCP, AWS, or Azure), coupled with DevOps practices, ensures seamless model deployment, scalability, and system reliability. This includes containerization, CI/CD pipelines, and infrastructure-as-code tools. Proficiency in programming languages such as Python, SQL, and Java. Who You Are 5+ years of industry experience working with machine learning tools and technologies. Familiarity with agile development frameworks and collaboration tools (e.g., JIRA, Confluence). Experience using Tensorflow, PyTorch, scikit-learn, Kubeflow, pandas and numpy. and frameworks like Ray, Dask preferred. Expertise in data engineering, object-oriented programming, and familiarity with microservices and cloud technologies. An ongoing learner who seeks out emerging technology and can influence others to think innovatively. Gets energized by fast-paced environments and capable of supporting multiple projects - can identify primary and secondary objectives, prioritize time, and communicate timelines to team members. Dedicated to fulfilling ideals of diversity, inclusion, and respect that the client aspire to achieve every day in every way. Regularly required to sit, talk, hear; use hands/fingers to touch, handle, and feel. Occasionally required to move about the workplace and reach with hands and arms. Requires close vision.

Roles and Responsibilities: Architecture & Infrastructure Design Architect scalable, resilient, and secure AI/ML infrastructure on AWS using services like EC2, SageMaker, Bedrock, VPC, RDS, DynamoDB, CloudWatch . Develop Infrastructure as Code (IaC) using Terraform , and automate deployments with CI/CD pipelines . Optimize cost and performance of cloud resources used for AI workloads. AI Project Leadership Translate business objectives into actionable AI strategies and solutions. Oversee the entire AI lifecycle from data ingestion, model training, and evaluation to deployment and monitoring. Drive roadmap planning, delivery timelines, and project success metrics. Model Development & Deployment Lead selection and development of AI/ML models, particularly for NLP, GenAI, and AIOps use cases . Implement frameworks for bias detection, explainability , and responsible AI . Enhance model performance through tuning and efficient resource utilization. Security & Compliance Ensure data privacy, security best practices, and compliance with IAM policies, encryption standards , and regulatory frameworks. Perform regular audits and vulnerability assessments to ensure system integrity. Team Leadership & Collaboration Lead and mentor a team of cloud engineers, ML practitioners, software developers, and data analysts. Promote cross-functional collaboration with business and technical stakeholders. Conduct technical reviews and ensure delivery of production-grade solutions. Monitoring & Maintenance Establish robust model monitoring , alerting , and feedback loops to detect drift and maintain model reliability. Ensure ongoing optimization of infrastructure and ML pipelines. Must-Have Skills: 10+ years of experience in IT with 4+ years in AI/ML leadership roles. Strong hands-on experience in AWS services : EC2, SageMaker, Bedrock, RDS, VPC, DynamoDB, CloudWatch. Expertise in Python for ML development and automation. Solid understanding of Terraform, Docker, Git , and CI/CD pipelines . Proven track record in delivering AI/ML projects into production environments . Deep understanding of MLOps, model versioning, monitoring , and retraining pipelines . Experience in implementing Responsible AI practices – including fairness, explainability, and bias mitigation. Knowledge of cloud security best practices and IAM role configuration. Excellent leadership, communication, and stakeholder management skills. Good-to-Have Skills: AWS Certifications such as AWS Certified Machine Learning – Specialty or AWS Certified Solutions Architect. Familiarity with data privacy laws and frameworks (GDPR, HIPAA). Experience with AI governance and ethical AI frameworks. Expertise in cost optimization and performance tuning for AI on the cloud. Exposure to LangChain , LLMs , Kubeflow , or GCP-based AI services .

Looking for MLOps Engineer to build and scale ML pipelines on AWS using SageMaker, EKS, Docker, and Terraform. Drive CI/CD, model tracking, automation & GPU-based training. Required Candidate profile Sagemaker, EKS, EC2, IAM, Cloudwatch, ECR, Docker, Kubernetes, Helm, Jenkins, Terraform, Kubeflow, Mlflow, Wandb Volcano Scheduler

Job Summary Director, Collection Platforms and AI As a director, you will be essential to drive customer satisfaction by delivering tangible business results to the customers. You will be working for the Enterprise Data Organization and will be an advocate and problem solver for the customers in your portfolio as part of the Collection Platforms and AI team. You will be using communication and problem-solving skills to support the customer on their automation journey with emerging automation tools to build and deliver end to end automation solutions for them. Team Collection Platforms and AI Enterprise Data Organizations objective is to drive growth across S&P divisions, enhance speed and productivity in our operations, and prepare our data estate for the future, benefiting our customers. Therefore, automation represents a massive opportunity to improve quality and efficiency, to expand into new markets and products, and to create customer and shareholder value. Agentic automation is the next frontier in intelligent process evolution, combining AI agents, orchestration layers, and cloud-native infrastructure to enable autonomous decision-making and task execution. To leverage the advancements in automation tools, its imperative to not only invest in the technologies but also democratize them, build literacy, and empower the work force. The Collection Platforms and AI team's mission is to drive this automation strategy across S&P Global and help create a truly digital workplace. We are responsible for creating, planning, and delivering transformational projects for the company using state of the art technologies and data science methods, developed either in house or in partnership with vendors. We are transforming the way we are collecting the essential intelligence our customers need to do decision with conviction, delivering it faster and at scale while maintaining the highest quality standards. What were looking for ? You will lead the design, development, and scaling of AI-driven agentic pipelines to transform workflows across S&P Global. This role requires a strategic leader who can architect end-to-end automation solutions using agentic frameworks, cloud infrastructure, and orchestration tools while managing senior stakeholders and driving adoption at scale. A visionary technical leader with knowledge of designing agentic pipelines and deploying AI applications in production environments. Understanding of cloud infrastructure (AWS/Azure/GCP), orchestration tools (e.g., Airflow, Kubeflow), and agentic frameworks (e.g., LangChain, AutoGen). Proven ability to translate business workflows into automation solutions, with emphasis on financial/data services use cases. An independent proactive person who is innovative, adaptable, creative, and detailed-oriented with high energy and a positive attitude. Exceptional skills in listening to clients, articulating ideas, and complex information in a clear and concise manner. Proven record of creating and maintaining strong relationships with senior members of client organizations, addressing their needs, and maintaining a high level of client satisfaction. Ability to understand what the right solution is for all type of problems, understanding and identifying the ultimate value of each project. Operationalize this technology across S&P Global, delivering scalable solutions that enhance efficiency, reduce latency, and unlock new capabilities for internal and external clients. Exceptional communication skills with experience presenting to C-level executives Responsibilities Engage with the multiple client areas (external and internal) and truly understand their problem and then deliver and support solutions that fit their needs. Understand the existing S&P Global product to leverage existing products as necessary to deliver a seamless end to end solution to the client. Evangelize agentic capabilities through workshops, demos, and executive briefings. Educate and spread awareness within the external client-base about automation capabilities to increase usage and idea generation. Increase automation adoption by focusing on distinct users and distinct processes. Deliver exceptional communication to multiple layers of management for the client. Provide automation training, coaching, and assistance specific to a users role. Demonstrate strong working knowledge of automation features to meet evolving client needs. Extensive knowledge and literacy of the suite of products and services offered through ongoing enhancements, and new offerings and how they fulfill customer needs. Establish monitoring frameworks for agent performance, drift detection, and self-healing mechanisms. Develop governance models for ethical AI agent deployment and compliance. Preferred Qualification 12+ years work experience with 5+ years in the Automation/AI space Knowledge of: Cloud platforms (AWS SageMaker, Azure ML; etc) Orchestration tools (Prefect, Airflow; etc) Agentic toolkits (LangChain, LlamaIndex, AutoGen) Experience in productionizing AI applications. Strong programming skills in python and common AI frameworks Experience with multi-modal LLMs and integrating vision and text for autonomous agents. Excellent written and oral communication in English Excellent presentation skills with a high degree of comfort speaking with senior executives, IT Management, and developers. Hands-on ability to build quick prototype/visuals to assist with high level product concepts and capabilities. Experience in deployment and management of applications utilizing cloud-based infrastructure. A desire to work in a fast-paced and challenging work environment Ability to work in a cross functional, multi geographic teams

Hi, We are hiring for ML Developer. Please find the attached JD. Key Responsibilities Design, develop, and optimize machine learning models for various business applications. Build and maintain scalable AI feature pipelines for efficient data processing and model training. Develop robust data ingestion, transformation, and storage solutions for big data. Implement and optimize ML workflows, ensuring scalability and efficiency. Monitor and maintain deployed models, ensuring performance, reliability, and retraining when necessary Preferred candidate profile Qualifications and Experience Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or a related field. 3.5 to 5 years of experience in machine learning, deep learning, or data science roles. Proficiency in Python and ML frameworks/tools such as PyTorch, Langchain Experience with data processing frameworks like Spark, Dask, Airflow and Dagster Hands-on experience with cloud platforms (AWS, GCP, Azure) and ML services. Experience with MLOps tools like ML flow, Kubeflow Familiarity with containerisation and orchestration tools like Docker and Kubernetes. Excellent problem-solving skills and ability to work in a fast-paced environment. Strong communication and collaboration skills.

Job description Job description Job Summary: We are looking for a results-driven and innovative Data Scientist with 46 years of experience in data analysis, machine learning, and product optimization. The ideal candidate will have a strong foundation in Python, SQL, and cloud services, along with practical exposure to GenAI, LLMs, and MLOps frameworks. You will be responsible for building scalable data pipelines, developing machine learning models, and solving real-world business problems with data-driven solutions. Key Responsibilities: Design and deploy LLM-powered solutions (e.g., RAG, LangChain, Vector DB) to enhance business processes. Build and fine-tune traditional ML models (Random Forest, Decision Trees) for predictive analytics. Optimize LLM performance using LoRA fine-tuning and post-training quantization. Develop and deploy containerized AI applications using Docker and FastAPI. Collaborate with agents like CrewAI and LangSmith to automate document processing and data extraction workflows. Implement Python & SQL-based ETL pipelines for real-time data ingestion. Design dashboards and KPI monitoring tools using Metabase to enable data-driven decision-making. Create data consumption triggers and automate reporting for international stakeholders. Moderate large-scale live virtual data science classes and provide operational support. Required Skills: Programming: Python, SQL, FastAPI ML & AI: Random Forest, Decision Trees, Clustering, PCA, DL, NLP, Transformers, Gen AI Frameworks/Tools: LangChain, MLflow, Kubeflow, CrewAI, LangSmith DevOps: Docker, Git Databases: MySQL, PostgreSQL, MongoDB Cloud: AWS (S3, EC2) Visualization: Metabase Other: Experience with LLM fine-tuning and quantization Role & responsibilitiesRole & responsibilities Preferred candidate profile

Role & responsibilities Develop, improve, and expand deployment pipeline infrastructure and services Build and deploy hyperscale machine learning pipelines into production Works closely with the developer teams to provide a self-service test integration, test coverage and quality Collaborate with the Security Engineering and Architecture teams to ensure code meets security requirements and is deployed in alignment with standards and governance processes Debug and resolve the issues developers encounter when attempting to use cloud services, with an eye toward continuously improving developer experience Execute against a vision of continuous integration and delivery that empowers developers Preferred candidate profile Building Infrastructure-as-code for Public Cloud using Terraform Experience in a Dev Ops engineering or equivalent role. Experience developing, enhancing, and maintaining CI/CD automation and configuration management using tools such as Jenkins, Snyk, and GitHub Experience with MLOps and orchestration tools such as Airflow, Kubeflow, Optuna, Mlflow or other similar MLOps tools. Experience with operationalizing and migrating ML models into production at scale Experience developing large scale model inference solutions using a parallel execution framework using Spark, EMR, or Databricks Experience developing complex orchestration and MLOps pipelines stitching together large volumes of data for training and scoring Proficiency with Apache Spark, EMR/DataProc and Cloud based tools (Snowflake, Redshift, EMR, Glue, Step Functions, Lambda, Step functions, AWS Batch, or similar etc.) Experience with ML libraries like H20, scikit learn and deep learning frameworks (PyTorch, TensorFlow, etc.) Familiarity with large scale production systems and technologies; for example load balancing, monitoring, distributed systems, and configuration management Development skills with at least one of the following programming languages: Python (preferred), Node.js, Bash. Knowledge of testing automation and code scanning tools Knowledge and experience with running distributed systems in public cloud environments such as AWS, Azure, and GCP. Knowledge of container orchestration platforms such as Kubernetes Knowledge of CDNs, DNS, web servers, and other supporting Internet services Source code management tools using Git. Experienced with standard Gitflow workflow Familiar with best practices for infrastructure security, reliability, and fault tolerance Passionate about automating and improving processes