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varun636

Final-year B.Tech CS student in Vijayawada seeking backend/infra/agent-systems roles, with experience in Python, systems programming, sandboxing, and agent safety tooling.

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Remote Vijayawada

Skills

PythonC/C++DockerLangGraphFastAPIWebSocketsNumbaSQLiteLinuxcgroupssandboxingAST parsingSSEconcurrent pipelinesagent systemsbackend engineeringinfra

Domains

backend infra agent systems agent safety evaluation systems programming automation fintech
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Location: Vijayawada, India
Remote: Yes (2pm-11pm IST — full EU hours + US-East mornings)
Willing to relocate: Yes
Available: Remote immediately, on-site from Dec 2026 — final-year B.Tech CS, MNNIT Allahabad
Technologies: Python, C/C++, Docker SDK (sandboxing, cgroups), LangGraph, FastAPI, WebSockets, Numba, SQLite (WAL), Linux
Resume: https://drive.google.com/file/d/1XXZ-d2hf8wEJYx0O-kX310qkeHrfYsR6/view?usp=sharing
Email: [email protected]
GitHub: https://github.com/sriramvarun0636
Systems plumbing, concurrent pipelines, and secure runtime isolation for agent startups. The claim I'd rather be judged on: not that my agents are correct, but that being wrong is discoverable. Write-up: https://sriramvarun0636.github.io/
Vasool — compliance-gated payment recovery agent on Razorpay's test APIs. I pre-registered seven falsification criteria before running anything, and then lost against one: a dumb baseline that retries everything recovers 16.35pp more than mine. It also breaks policy in 1,000 of 1,000 seeded runs; mine breaks it in 0. That trade is the finding, and it's the second section of the README, not an appendix. The LLM never calls a tool — inert verdict type, no adapter to the execution plane, asserted by an import-graph test. Simulated outcomes, tagged as such in the source; every figure is a key in a committed manifest, so you can check any number without running anything.
https://github.com/sriramvarun0636/Vasool — 5-min walkthrough: https://youtube.com/watch?v=B0Iov6qAaqs
AutoPatch-AI — autonomous code remediation agent on a LangGraph state machine. Zero-trust Docker execution layer (network_disabled, 256MB RAM, 128 PID cgroup caps), real-time telemetry over thread-safe queues via SSE, AST-based parsing that cut context bloat ~80%.
https://github.com/sriramvarun0636/AutoPatch-AI — 90s sandbox demo: https://www.loom.com/share/104cf5ebcfc144a09f49c62830755408
SentinelPrime — multi-threaded options system on live WebSocket data. Releases the GIL via Numba (nogil=True), actor model over daemon threads isolating DB I/O and API calls, fixed-size ring buffers to kill allocation overhead under 24/7 operation.
https://github.com/sriramvarun0636/SentinelPrime
Looking for backend/infra/agent-systems work at pre-seed to Series A — founding engineer or engineer #2-5. Eval and agent-safety teams too. Standard loops are fine, and I'll also take a paid 2-3 week trial sprint on a real bottleneck in your codebase.

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