GenAI Data Scientists (Lead and Expert)

An accomplished Principal / Expert Data Scientist with 10+ Year of experience and deep expertise & hands-on experience in classical machine learning, GenAI applications, and the ML lifecycle.
The candidate will have responsibilities across the following functions:
Machine Learning and Statistical Modelling:
Build and optimise 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.
GenAI and 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 to agentic systems and multi-agent workflows.
Agentic Systems and 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.
Cloud ML-Ops and 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).
Leadership and Collaboration:
Lead technical direction for AI solutions.
Work with product teams to define AI features.
Requirements:
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, GitOps.
Experience building production-grade GenAI applications.
Skilled in EDA, DOE, and model evaluation metrics for identifying data patterns, validating hypotheses, and improving model quality.

Experience
9-13 yrs