I build production backend and AI systems for teams that need execution fast: Kafka pipelines, high-throughput APIs, distributed data platforms, RAG systems, and internal automation tools.

Backend ownership        Java · Python · Golang · Spring Boot · Django · REST APIs
Data infrastructure      Kafka · Kafka Connect · Debezium · Snowflake · MongoDB · Redis
Cloud + reliability      AWS · Kubernetes · Docker · Prometheus · Grafana · CI/CD
Applied AI               RAG · LLM gateways · agent orchestration · observability

I care about systems that are boring in production and impressive under load: clean service boundaries, measurable latency wins, safe migrations, observable pipelines, and APIs that don't collapse when usage spikes.

• Built DataPulse, an enterprise CDC platform moving MongoDB data into Snowflake using Debezium, Kafka Connect, and Snowflake Sink connectors.
• Deployed a 3-replica Kafka Connect cluster on Kubernetes with Strimzi and optimized producer configs to reduce network overhead by 40%.
• Architected a MongoDB-backed Taxonomy Service serving 150K+ requests/sec at peak with 98% cache hit rate.
• Reduced average API response time by 70% using version-aware caching and read-optimized data modeling.
• Worked across engineering teams at Amazon, Allen Digital, Fivetran, and Observe.AI.

• Targeting Senior SDE / Senior Backend Engineer roles, especially remote USD opportunities.
• Taking selective freelance contracts around AI apps, RAG systems, backend APIs, data pipelines, and internal automation.
• Building AI infrastructure projects that are useful, deployable, and demo-worthy.