Principal Data Scientist - S U

Required Skills:

• Classical ML

• GenAI / LLM

• Python

• ML-Ops

• Agentic AI

• Cloud Platform

• SQL

• Docker / Git-Ops

Nice to Have:

• Statistical Modelling

• Memory Architectures

JD #1 Experience Level: 10+ Years | Classical ML + GenAI+ ML-Ops 1 Position Overview An accomplished Principal / Expert Data Scientist with 10+ Year of experience and having deep expertise & hands-on in classical machine learning, GenAI Applications & ML lifecycle, Key Responsibilities 1. Machine Learning & Statistical Modelling Build and optimize complex ML models: regression, classification, clustering, sequence models, time series forecasting. Lead sophisticated feature engineering and data quality analysis. Apply statistical modelling techniques, experimental design, and Performance evaluation. Develop scalable and maintainable ML pipelines for structured and unstructured data. 2. GenAI & LLM Systems Architect and develop LLM-based applications using SOTA LLM’s. Build RAG pipelines using vector databases (faiss, aisearch, opensearch, PG vector etc). Integrate GenAI systems with enterprise apps, APIs, and data sources. Model Context Protocol (MCP) & Tooling Exposure of Agentic systems and multi-agent workflows 3. Agentic Systems & Model Context Protocol (MCP) Exposure to agentic system design, including tool-calling workflows, planner–executor patterns, and multi-agent coordination. Integrate memory architectures such as episodic, semantic, and vector-based long-term memory within agent workflows. Implement and manage Model Context Protocol (MCP) servers to enable seamless connectivity between LLMs, tools, APIs, and enterprise applications. Collaborate with engineering teams to build reliable, extensible agent tooling and ensure smooth integration into production environments. 4. Cloud ML-Ops & Quality ML Modelling, data drift, concept drift, model quality monitoring. Hands‑on experience across AWS/ Azure/ Databricks, with flexibility to work on any cloud platform. Adhere to stringent quality assurance and documentation standards using version control and code repositories (e.g., Git, GitHub, Markdown) 5. Leadership & Collaboration Lead technical direction for AI solutions. Work with product teams to define AI features. Required Skills & Experience 10+ years in Classical ML, GenAI & ML-Ops. Strong experience in: Python, PySpark, SQL, Scikit-Learn, XGBoost, LightGBM, Random Forest LangChain, LangGraph, LangSmith (tracing, metrics, evaluations) MLflow / Sagemaker / Databricks Docker, Git-Ops Experience building production-grade GenAI applications. Skilled in EDA, DOE, and model evaluation metrics for identifying data patterns, validating hypotheses, and improving model quality