Baji Shaik
Professional Summary
AI Orchestration Engineer with 3 years of experience designing multi-agent orchestration, RAG platforms, and large-scale data pipelines for enterprise AI. I build agent frameworks (LangChain) and Model Context Protocol servers (Python, Pydantic) that provide access-controlled, auditable interfaces into backend systems. My data-engineering work focuses on PySpark on Databricks and Apache Hudi to deliver retrieval-optimized data products and fast incremental processing for AI consumption. I implement CI-gated evaluation pipelines with LLM-as-judge and Langfuse observability to enforce deployment quality, and design Responsible AI guardrails to block prompt injection and data exfiltration. I combine platform ownership, governance-first design, and measurable performance improvements to productionize agentic AI in regulated environments.
Technical Skills
Work Experience
- Architected a multi-agent orchestration framework using LangChain to coordinate 8 specialised agents with a registry pattern that auto-generated downstream configs, increasing routing accuracy from ~85% to 95%.
- Built a Model Context Protocol (MCP) server from scratch with Python and Pydantic, exposing 23 tools with per-agent access control to enable safe, auditable agent access to enterprise backends.
- Implemented an enterprise RAG platform that integrated SharePoint knowledge sources with Azure AI Search for hybrid semantic and keyword retrieval and citation grounding used in internal workflows.
- Led migration of 35 legacy Airflow DAGs to PySpark jobs on Databricks, reducing daily pipeline runtime from roughly 12–13 hours to about 2 hours 15 minutes on the production workload.
- Adopted Apache Hudi for ACID-managed data lake storage to enable fast incremental processing, simplify pipeline recovery, and support reliable downstream retrieval for agents.
- Established CI-gated evaluation pipelines combining rule-based checks and an LLM-as-judge to block deployments below quality thresholds; integrated Langfuse for observability across dev, staging and production.
- Engineered retrieval-optimized data products and schemas with SQL transformations and multi-source validation to provide reliable, structured inputs for agent workflows and RAG retrieval.
- Integrated Kafka for streaming ingestion to feed near-real-time agent inputs and reduced end-to-end data latency for time-sensitive orchestration use cases.
Education
Certifications
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