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.

- Robust 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

📌 10+years, ML, Agentic AI Engineer, Enterprise AI Solutions (Bengaluru)
🏢 Fractal Analytics
📍 Bengaluru