Karthik O

About

Built and productionized an LLM-powered support/knowledge pipeline using embeddings and retrieval (RAG) to deliver more grounded, higher-quality responses while reducing manual effort. Focused on real-world reliability and performance—adding structured validation/guardrails, optimizing vector search and context size for latency/scale, and monitoring failure patterns in production. Experienced with orchestration via LangChain for LLM workflows and Airflow for production data/ML pipelines, and iterates closely with operations stakeholders through demos and feedback.

Key Strengths

• Built and deployed a production LLM-powered support pipeline using embeddings + retrieval for grounded responses
• Improved reliability via structured output validation, prompt controls, and retrieval-based grounding
• Performance optimization for latency/scale (reduced retrieval work, minimized context, efficient vector search, workflow reuse)
• Practical evaluation approach using realistic test sets and metrics (quality, correctness, latency, failure rate)
• End-to-end workflow design with guardrails, component-level testing, and production monitoring
• Effective collaboration with non-technical operations stakeholders through regular check-ins and demos

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