Tarun Gowda

About

GenAI/LLM engineer who built and productionized a 0-1 application (EMULaiTOR at Lumanity) combining qualitative + quantitative data using Postgres/pgvector RAG and prompt engineering, deployed with Azure backend and AWS-hosted frontend. Demonstrates strong production instincts (latency reduction via region alignment, autoscaling/health checks) and hands-on agent/tool-call debugging, plus experience enabling sales and winning a large pharma client.

Experience

Education

Key Strengths

• Built and shipped a 0-1 GenAI product (EMULaiTOR) integrating PDF/text, qualitative, and quantitative data into an aggregated LLM output with supporting sources and charts
• Strong RAG + vector database implementation experience (Postgres + pgvector; top-k retrieval) and prompt engineering to synthesize results
• Pragmatic data quality approach for LLM systems (handling empty rows/columns; ensuring complete inputs to reduce hallucinations/unwanted answers)
• Production scalability/resilience mindset (region co-location to reduce latency, health checks, load balancer failover, horizontal scaling, async + autoscaling rules)
• Effective real-time debugging of agentic workflows via tracing, flags, structured API errors, and tool-call monitoring (identified wrong-tool selection issue and fixed logic/context)
• Able to lead and tailor technical workshops for mixed audiences (separate developer vs QA content) and lead a team despite being less experienced due to domain expertise
• Supports sales enablement and deal closing by translating technical differentiators for customer pitches (helped win a large pharma client)