darshannere

Hi! This looks like a great fit, could you please share the best way to apply for the role?

Location: San Francisco, CA, USA

Remote: Yes (remote or hybrid)

Willing to relocate: Yes (SF, NYC, Austin, other US hubs)

Technologies: Python, TypeScript, Java, C++, FastAPI, NestJS, Next.js, React, Node.js, PostgreSQL, SQLite, Docker, AWS, PyTorch, Hugging Face, TRL, LangChain, LangGraph, LLM APIs (OpenAI/Anthropic), RAG, fine-tuning (GRPO/DPO/LoRA), multi-agent systems, observability, REST APIs, Git, CI/CD

Résumé/CV: https://darshannere.com/resume | https://github.com/darshannere

Email: darshan.nere2@gmail.com

Most AI agents in production are black boxes. When one fails, nobody can tell you why. I build the tooling that fixes that.

Tracea (tracea.dev): open-source observability for AI agents. Traces every LLM call by patching the HTTP transport layer, so there are no SDK wrappers, no framework lock-in, no code changes. Install and you see everything.

ObservAgent: local-first observability dashboard for Claude Code sessions. 1000+ active users, open source.

Before this: shipped a civic complaint platform to 50k+ users for a municipal government.

Looking for SWE engineer or AI engineer roles at startups building real production AI. High ownership, real users, hard problems.

Location: San Francisco, CA, USA

Remote: Yes (open to hybrid or remote)

Willing to relocate: Yes (SF, NYC, Austin, or other US tech hubs)

Technologies: Python, JAVA, C++, FastAPI, TypeScript, React, PostgreSQL, SQLite, Docker, AWS, PyTorch, LLM APIs (OpenAI/Anthropic), LangChain

Résumé/CV: https://darshannere.com | https://github.com/darshannere

Email: darshan.nere2@gmail.com

I build AI agent infrastructure. Most recently: Tracea (tracea.dev), an open-source observability platform for AI agent teams that patches the HTTP transport layer to trace LLM calls without any framework changes — no SDK wrappers, no code changes, just install and instrument. Also built ObservAgent (1000+ active users, open source).

Research side: co-authored a paper on failure-aware penetration testing - training LLMs to emit typed failure tokens so failure recognition becomes a structured recovery signal rather than an implicit state.

Prior: full-stack engineer shipping software to 50k+ users.

Looking for full-stack or AI/ML engineering roles at early-stage or growth-stage companies working on production AI systems - LLM integration, agent infrastructure, or observability. High ownership, real users, hard problems.

Location: Blacksburg, VA, USA

Remote: Yes (open to hybrid or remote)

Willing to relocate: Yes (SF, NYC, Austin, or other US locations)

Technologies: Python, FastAPI, TypeScript, React, PostgreSQL, SQLite, Docker, AWS, PyTorch, LLM APIs (OpenAI/Anthropic), LangChain

Résumé/CV: https://darshannere.com | https://github.com/darshannere

Email: darshan.nere2@gmail.com

I build AI agent infrastructure, most recently Tracea (tracea.dev), an open-source observability platform for AI agent teams that patches the HTTP transport layer to trace LLM calls without framework changes. Also built ObservAgent (300+ users, open source) and published research on GRPO fine-tuning for medical reasoning (+11% accuracy over SFT baseline).

Looking for full-stack or AI/ML engineering roles at early-stage startups or growth-stage companies where I can work on production AI systems with high ownership and real impact. Strong preference for roles involving LLM integration, agent infrastructure, or AI observability.