Role Overview KPIT is seeking a highly skilled, hands-onSenior Data Scientist / AI Engineer(Remote)to architect, develop, and deliver end-to-end Machine Learning and AI solutions within the Azure ecosystem.The successful candidate will be responsible for the entire AI lifecycle—from designing scalable data pipelines to deploying production-grade AI agents capable of complex, multi-step reasoning. This role is ideal for an engineer who excels at bridging the gap between experimental data science and robust, scalable MLOps.Key Responsibilities End-to-End Solution Delivery:Own the full lifecycle of AI/ML solutions, from initial problem definition and data acquisition to production deployment and monitoring. Data Engineering & Pipeline Development:Build and maintain high-performance, scalable data pipelines using PySpark and Spark on Azure Databricks. Model Development & Feature Engineering:Perform advanced feature engineering, data preparation, and sophisticated model development for diverse business use cases. Agentic AI & GenAI:Develop and deploy AI agents and workflows utilizing multi-step reasoning, tool usage, and Generative AI frameworks. MLOps & Lifecycle Management:Implement and manage MLflow for experiment tracking; monitor model performance, data drift, and system reliability in production environments. Cross-Functional Collaboration:Partner with Business, Data Engineering, and Analytics teams to translate business requirements into technical AI solutions. Technical Leadership:Act as a subject matter expert, mentoring junior team members and driving engineering best practices.Required Skills & Qualifications Programming & Data:Expert-level proficiency in Python and SQL. Big Data & Cloud:Strong hands-on experience with PySpark, Apache Spark, and Azure Databricks. Machine Learning:Deep understanding of ML workflows, model evaluation techniques, and statistical modeling. MLOps:Practical experience with MLflow and fundamental MLOps principles (CI/CD for ML, model versioning, and monitoring). Generative AI:Proven exposure to Large Language Models (LLMs) and AI agent frameworks (e.g., LangChain, Semantic Kernel, or AutoGen). Education:Bachelor's or Master's degree in Computer Science, Data Science, Engineering, or a related quantitative field.Experience Requirements Professional Experience:5–6 years of hands-on experience in Data Science, Machine Learning, or AI Engineering. Production Experience:A proven track record of deploying and maintaining ML/AI models in a production environment. Cloud Expertise:Extensive experience working within cloud environments, specifically Microsoft Azure.About KPIT KPIT is a global partner to the automotive and mobility ecosystem. We deliver end-to-end engineering, data, and AI-led digital transformation solutions to leading Original Equipment Manufacturers (OEMs) and Tier-1 suppliers worldwide, shaping the future of mobility.