Principal / Expert Data Scientist

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.

1. 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

1. Agentic Systems & Model Context Protocol (MCP)

 Exposure to agentic system design, including toolcalling workflows, planner–executor

patterns, and multiagent coordination.

 Integrate memory architectures such as episodic, semantic,

and vectorbased longterm 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.

1. Cloud ML-Ops & Quality

 ML Modelling, data drift, concept drift, model quality monitoring.

 Handson 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)

1. 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