10+years, ML, Agentic AI Engineer, Enterprise AI Solutions

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 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. 

4. 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) 

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