Required Skills:
- Machine Learning
-
NLP / GenAI
-
Python
-
Cloud ML Platforms
-
Data Wrangling
-
Statistical Modelling
-
SQL / Databases
-
MLOps / Version Control
Nice to Have:
- Data Visualization
-
API Development
Expert Data Scientist Experience Level: 10+ Years | Classical ML + GenAI+ ML-Ops ________________________________________ 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: o Python, PySpark, SQL, Scikit-Learn, XGBoost, LightGBM, Random Forest o LangChain, LangGraph, LangSmith (tracing, metrics, evaluations) o MLflow / Sagemaker / Databricks o 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
