Python and AI Data Engineering Lead - Senior Vice President

12+ years of Experience in Data Domain , Expert at handling large Volume Data, Candidate will Individual Contributor

Design, develop, and maintain ETL processes to extract, transform, and load data from various sources into our data warehouse. Write complex SQL queries and PL/SQL scripts to perform data manipulation, validation, and transformation. Develop and maintain data pipelines using Python and related libraries (e.g., Polar ,Pandas). Optimize ETL processes and data pipelines for performance and scalability. Collaborate with data analysts and other stakeholders to understand data requirements and develop solutions to meet their needs. Implement data quality checks and monitoring to ensure data accuracy and consistency. Troubleshoot and resolve data-related issues. Create and maintain technical documentation for ETL processes, data pipelines, and database solutions. Stay up-to-date with the latest trends and technologies in data management and analytics.

Database Knowledge: Basic knowledge of databases such as Oracle/SQL. Scripting: Basic knowledge of Python. Problem-Solving: Excellent analytical and problem-solving skills. Communication: Strong communication and interpersonal skills. Teamwork: Ability to work effectively in a team environment. Methodology of AI includes Chunking, Embedding, AI Engineering, Prompt Engineering

Deep hands-on experience in engineering and executing scalable enterprise solutions.

Expert-level proficiency in Python (e.g., FastAPI, Flask, PySpark) or Java (e.g., Spring Boot, Spring Cloud, Spring Security).

Proficiency in UI (e.g., Angular, React, Node.js, TypeScript) for full-stack development.

Proficiency in database technologies, such as Oracle, Postgres, or MongoDB.

Solid understanding of core AI concepts, including knowledge representation, automated planning, decision-making under uncertainty, and multi-agent systems.

Hands-on experience with relevant frameworks (e.g., Google ADK, LanGraph, LangChain, AutoGen, CrewAI, N8N).

Extensive experience with machine learning frameworks (e.g., TensorFlow, PyTorch) and relevant libraries (e.g., Scikit-Learn, NumPy, Pandas).

Proven experience in creating, deploying, and integrating MCPs (Model Context Protocol) into agentic AI systems.

Deep familiarity with large language models (LLMs) such as ChatGPT, Claude, Gemini, and Llama, including their application within agentic systems.