Role Overview
KPIT is seeking a highly skilled, hands-on Senior 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.