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keerthana medaveni
Mid-Level AI/ML Software Engineer specializing in agentic LLM systems
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Built and deployed a production LLM-powered multi-agent compliance copilot (life sciences/finance) using LangChain/LangGraph + RAG over vector databases, delivered via async FastAPI on Kubernetes. Emphasizes audit-ready, deterministic outputs with schema constraints and citations, plus rigorous evaluation/monitoring; reports 60%+ reduction in manual research time and successful production adoption.
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Key Strengths
- Built and deployed production LLM multi-agent copilot for compliance-heavy domains (life sciences, finance)
- Reduced manual research time by 60%+ through grounded RAG-based document search
- Hallucination mitigation via strict retrieval grounding, confidence thresholds, and 'not found' safe-fail behavior
- Deterministic, audit-ready outputs using schema constraints (MCP) with required citations
- Designed explicit multi-agent workflows with state transitions, routing, and fallback paths (LangGraph)
- Improved latency predictability using async execution and inference optimization
- Strong evaluation discipline: gold datasets, adversarial cases, offline metrics, and regression testing
- Production reliability focus with observability and end-to-end tracing
- Effective collaboration with non-technical compliance/operations stakeholders via workflow-driven design and expectation-setting
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