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Ganesh Bandi
Mid-level AI Engineer specializing in LLMs, RAG, and MLOps
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About
LLM engineer who has deployed production RAG systems for regulated document QA (PDFs/knowledge bases), emphasizing grounded answers with citations, RBAC, monitoring, and continuous feedback. Demonstrates deep practical expertise in retrieval quality (semantic chunking, hybrid BM25+embeddings, re-ranking), reliability (guardrails, deterministic workflows), and measurable evaluation (golden sets, log replay, A/B tests) while partnering closely with compliance/operations stakeholders.
Experience
Education
Key Strengths
- Built and deployed production LLM RAG systems over regulated documents with grounded, cited answers
- Improved retrieval quality via semantic/structured chunking, metadata, hybrid BM25+embedding search, and re-ranking
- Reduced hallucinations using guardrails, citation enforcement, schema constraints, and validation steps
- Designed deterministic multi-step agent workflows with branching/retries/state management (LangGraph/state machines)
- Production focus on latency and cost optimization (prompt tuning, caching) with observability/monitoring
- Strong evaluation discipline: unit tests per step, golden queries, log replay, A/B testing, user feedback loops
- Effective cross-functional collaboration with operations/compliance; translated AI into audit-friendly search with metrics
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