AI / LLM systems
RAG pipelines, agentic workflows, model gateways, orchestration, fallbacks, and observability across providers.
Software Engineer
I build robust backends and AI systems.
From launching core payment flows to scaling AI platforms.
I'm a software engineer focused on the logic customers actually hit. I've built multi-model AI gateways, distributed schedulers, and high-throughput systems at companies like Jupiter Money and Amazon.
Previously at
AmazonPrime Video
JupiterFintech
Allen DigitalEdtech & AI
Built systems used by millions at Amazon, Jupiter Money, and Allen Digital
What I can build
Concrete systems and ownership—not slide decks.
Scalable backends
Microservices, event-driven design, and APIs built to stay fast under real traffic—not just on slides.
MVP → production
Take ideas to reliable launches: schema design, rollout strategy, and the operational glue teams forget.
High-throughput infra
Kafka-style pipelines, distributed schedulers, and systems tuned for millions of jobs or requests per day.
Selected impact
A few high-signal systems—each tied to volume, reliability, or measurable outcomes.
Allen Digital · AI Platform
AI gateway & agentic workflows
- Built a Model Gateway Platform routing traffic across multiple AI providers (Bedrock, OpenAI).
- Designed agentic learning workflows that lifted automated doubt resolution from 60% to 90%.
- requests / day
- 1M+
- requests / day
- automated resolution
- 60→90%
- automated resolution
Jupiter Money · Payments & Infrastructure
Core fintech systems
- Architected a Bills Payment Service handling 200K+ monthly transactions.
- Designed a distributed scheduler processing 10M+ daily jobs with 70% lower database load.
- monthly transactions
- 200K+
- monthly transactions
- scheduler reliability
- 99.8%
- scheduler reliability
Amazon Prime Video · Growth
International expansion
- Built scalable backend services for payments and subscriptions to support international market expansion.
- Developed automated bulk subscription upgrade workflows to massively improve the customer experience.
- user acquisition
- +20%
- user acquisition
- revenue increase
- +60%
- revenue increase
Stack
Tools and patterns I use in production
- Backend
- Go, Java, Kotlin, Python
- Systems
- Distributed systems, microservices, event-driven
- AI
- LLM systems, RAG, agent workflows
- Infra
- AWS, Kubernetes, Kafka
Have an idea or need a system that scales? Let’s build it.
Typical engagements: fractional lead, architecture review, or hands-on build for high-stakes launches.
Technical Jargon Level