data engineering , Apis, Sql, Backend Development, system integration, Python, embeddings, reusable AI frameworks, data pipelines, LLMs, AI governance frameworks, workflow orchestration, event-driven architectures, vector databases, prompt engineering, automation platforms, enterprise AI platforms, RAG, AI evaluation, semantic search, observability, intelligent agents

Tensorflow, Pytorch, Api Development, Python, ETL and Data Pipelines, Model Deployment, Scikit-learn, Prompt Engineering

Architecting complex multi-agent multi-model AI systems at scale, Full-stack AI expertise data engineering model training fine-tuning LLMOps infrastructure, Owns architecture decisions and design reviews ADRs, Mentors and upskills engineers, Technical leadership of AI engineering workstreams or a small team, Cross-functional partnership with Product Data Science Security and Compliance, Cost and performance optimization of AI systems in production

Model Context Protocol (MCP), Retrieval-Augmented Generation (RAG), API design, Python, Microservices, AWS, LLM optimization techniques, agent development frameworks, fine-tuning, Distillation, quantization

snowflake , Pyspark, Containers, Api Integration, Sql, Databricks, Azure, Python, LangChain, CI CD monitoring, LLMs, Claude, retrieval optimisation, GPT, LangGraph, RAG pipelines, OpenAI

Java, Cursor, Sql, Git, Javascript, Shell Script, Rest Assured, Selenium, Python, AWS Bedrock, Playwright, AI-first mindset, Claude, K6, GitHub Copilot, RAG Retrieval-Augmented Generation, Code Codex