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.