Distributed Systems & GenAI Engineer for SaaS Startups
Built for SaaS teams past the point where “it works on staging” is good enough. Kafka pipelines that don't drop messages under load. RAG systems that don't lose context by turn five.
Featured Work
A context-aware agentic RAG system that retrieves, reasons, and acts across multi-turn conversations without losing context, backed by MongoDB Atlas Vector Search.
Answers stay grounded across the whole conversation.
A high-throughput social listening pipeline ingesting ~20,000 posts/hour from Reddit and social platforms for real-time lead generation.
Lead discovery moved from hourly batches to near real-time.
A productized pipeline that turns a sales call recording into structured CRM fields — transcript, LLM extraction, and a webhook straight into your CRM.
CRM fields populate before the call ends.
What I Do
Distributed Systems & Backend
- Kafka pipelinesingestion that doesn't fall over at 20k events/hr
- Redis caching layershot paths that stay fast when traffic spikes
- High-throughput APIsendpoints that hold under concurrent load
- Sales call → CRM pipelinesrecordings landing as structured CRM fields
- Webhook integrationsdeliveries that retry instead of vanishing
- Observability (OpenTelemetry)traces that show where the latency went
GenAI & AI Infrastructure
- RAG pipelinescontext that survives a multi-turn conversation
- LLM agentstool calls that fail loudly, never silently
- Vector search (MongoDB Atlas, Pinecone)relevance that holds as the corpus grows
- Transcript processingraw call audio to clean, queryable text
- Structured data extractionoutput that matches your schema every time
- Evals & monitoringregressions caught before your users find them