I partner with engineering teams to turn complex technical problems into shipped solutions — from API integrations to production-grade AI deployments. With a background in green tech, I bring domain depth to the problems that actually matter.
I'm Rakesh Suryavanshi, a forward deployed engineer with an MS in Computer Science from Cal State LA. I've spent 8+ years building production software across the full stack.
Senior software engineer with 8+ years shipping full-stack systems, now focused on moving into Forward Deployed Engineering — where deep technical skills meet direct customer impact. My sweet spot is the gap between a product vision and working software.
My background is in green tech — I've built software for sustainability, energy, and climate-adjacent domains, which means I understand complex regulated industries and the engineering trade-offs that come with them. Currently sharpening my edge on AI/LLM deployments, API integrations, and DevOps.
Production-grade Retrieval-Augmented Generation service with strict multi-tenant data isolation. Async-first — FastAPI, Celery, SSE streaming, Redis caching, pgvector HNSW. Zero API costs via local Ollama.
Full-stack healthcare portal — live appointments, discharge summaries, lab results, email notifications, jQuery chat, Spring Security auth.
Data acquisition from Twitter & Facebook APIs. MongoDB storage, Amazon Elasticsearch for indexing, real-time social analytics visualisation.
Concurrent web crawler with HTML parsing, JSON metadata extraction, full-text indexing and search via Apache Lucene with multithreaded request handling.
Custom ASP.NET image editor with watermark overlay, colour/font/size controls, Java Servlet backend and JSP/JSTL frontend with Bootstrap UI.
From enterprise Java systems to modern AI/LLM pipelines and cloud infrastructure — technologies I've used in production.
I review the codebase before the first meeting. The stated problem is rarely the real one.
Crisp spec, clear success criteria, explicit boundaries. No surprises mid-engagement.
Bias toward shipping and iterating. Observability and rollback plans from day one.
Docs your team reads. Code your team maintains. Done when the next engineer owns it.
Hard integration, AI product to productionise, or infrastructure that needs to actually work — let's talk. Available within 1–2 weeks.