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Pavan Kalyan
Mid-level AI Engineer specializing in GenAI agents and RAG for IT operations
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About
Built and operates a production LLM agent for enterprise IT operations that triages and drafts resolutions for high-volume ServiceNow tickets using LangChain + RAG (Pinecone/pgvector) and AWS Bedrock/OpenAI. Emphasizes reliability with schema-validated stages, offline eval datasets from real tickets, and CloudWatch-driven monitoring/guardrails; system scales to 40K+ tickets/month and cut resolution time ~28%.
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Key Strengths
- Built and deployed LLM-powered IT ticket intake/resolution assistant for ServiceNow in production
- Designed agent-based, multi-step orchestration (classification → retrieval → response generation) with clear tool boundaries
- Implemented RAG over internal runbooks/SOPs/postmortems with Pinecone and pgvector plus metadata filtering
- Production guardrails to reduce hallucinations (strict prompts, confidence thresholds, fallbacks)
- Strong evaluation discipline: offline test sets from historical tickets + regression testing for prompt changes
- Operational monitoring with structured logs/CloudWatch and automated fallback based on thresholds
- Scaled containerized services on AWS (Docker + Lambda + API Gateway) handling 40K+ tickets/month
- Delivered measurable impact: ~28% reduction in average ticket resolution time; ~22% reduction in false automation triggers
- Effective collaboration with non-technical ops stakeholders; translated SLA/triage needs into system rules and metrics
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