I build the systems plumbing, concurrent pipelines, and secure runtime isolation that AI coding-agent startups are racing to ship. Happy to prove it directly on your codebase first — I'll take on a 2-3 week paid trial sprint on a real backend bottleneck before any longer-term conversation.
• AutoPatch-AI (<a href="https://github.com/sriramvarun0636/AutoPatch-AI" rel="nofollow">https://github.com/sriramvarun0636/AutoPatch-AI): Autonomous, self-healing code remediation agent built on a LangGraph state machine. Engineered a zero-trust Docker execution layer (network_disabled=True, 256 MB RAM / 128 PID cgroup hard limits), streamed real-time telemetry over thread-safe queues via SSE, and built an AST-based line-level parser (cutting context/generation bloat by ~80%). Achieved a 100% resolution rate (5/5) on a custom Micro-SWE-bench harness with an average time-to-resolution of ~205 seconds.
• SentinelPrime (<a href="https://github.com/sriramvarun0636/SentinelPrime" rel="nofollow">https://github.com/sriramvarun0636/SentinelPrime): Multi-threaded, regime-aware options trading system for live WebSocket market data. Releases the CPython GIL during numerical computations using Numba JIT compilation (nogil=True) and an Actor Model architecture over daemon threads to isolate DB I/O and API calls. Built thread-safe O(1) queues and fixed-size circular memory buffers to eliminate allocation overhead under 24/7 continuous operation.
• HydraPrime (<a href="https://github.com/sriramvarun0636/HydraPrime" rel="nofollow">https://github.com/sriramvarun0636/HydraPrime): Systematic XAUUSD strategy in MQL5 (C++ derivative), developed and iterated solo over two years — OTE/Fair-Value-Gap structural entries with ATR-adaptive sizing and multi-layered drawdown circuit breakers. Backtested across 96M+ real ticks over a single two-year window (2024–2026) that included a strong gold bull run: 1.37 profit factor, 13.8% max drawdown, 252% net return. Not yet forward-tested live, so I hold that return number loosely until it survives a different regime.
What I'm looking for:
Backend/infra/agent-systems roles at early-stage teams, pre-seed to Series A — founding engineer or engineer #2–5, wherever you're at. Standard interview loops work fine too, not just trial sprints.
varun636 · · focus · HN ↗
Remote: Yes (worldwide — 2pm–11pm IST, overlapping full EU business hours plus US-East mornings)
Willing to relocate: Yes
Technologies: Python, C/C++, Docker SDK (Sandboxing/cgroups), LangGraph, Numba JIT, POSIX, FastAPI, WebSockets, Thread-Safe Queues, SQLite (WAL), Linux
GitHub: <a href="https://github.com/sriramvarun0636" rel="nofollow">https://github.com/sriramvarun0636
LinkedIn: <a href="https://www.linkedin.com/in/sri-ram-varun-dittakavi-4103a428a/" rel="nofollow">https://www.linkedin.com/in/sri-ram-varun-dittakavi-4103a428...
Resume/CV: <a href="https://drive.google.com/file/d/1XXZ-d2hf8wEJYx0O-kX310qkeHrfYsR6/view?usp=sharing" rel="nofollow">https://drive.google.com/file/d/1XXZ-d2hf8wEJYx0O-kX310qkeHr...
90s Sandbox Demo: <a href="https://www.loom.com/share/104cf5ebcfc144a09f49c62830755408" rel="nofollow">https://www.loom.com/share/104cf5ebcfc144a09f49c62830755408
Email: sriramvarun636@gmail.com
I build the systems plumbing, concurrent pipelines, and secure runtime isolation that AI coding-agent startups are racing to ship. Happy to prove it directly on your codebase first — I'll take on a 2-3 week paid trial sprint on a real backend bottleneck before any longer-term conversation.
Computer Science Engineer (MNNIT Allahabad). Formerly Software Engineering Intern @ DeeveloX (preprocessed 5GB+ vector search datasets with Gemini APIs).
Recent High-Agency Projects:
• AutoPatch-AI (<a href="https://github.com/sriramvarun0636/AutoPatch-AI" rel="nofollow">https://github.com/sriramvarun0636/AutoPatch-AI): Autonomous, self-healing code remediation agent built on a LangGraph state machine. Engineered a zero-trust Docker execution layer (network_disabled=True, 256 MB RAM / 128 PID cgroup hard limits), streamed real-time telemetry over thread-safe queues via SSE, and built an AST-based line-level parser (cutting context/generation bloat by ~80%). Achieved a 100% resolution rate (5/5) on a custom Micro-SWE-bench harness with an average time-to-resolution of ~205 seconds.
• SentinelPrime (<a href="https://github.com/sriramvarun0636/SentinelPrime" rel="nofollow">https://github.com/sriramvarun0636/SentinelPrime): Multi-threaded, regime-aware options trading system for live WebSocket market data. Releases the CPython GIL during numerical computations using Numba JIT compilation (nogil=True) and an Actor Model architecture over daemon threads to isolate DB I/O and API calls. Built thread-safe O(1) queues and fixed-size circular memory buffers to eliminate allocation overhead under 24/7 continuous operation.
• HydraPrime (<a href="https://github.com/sriramvarun0636/HydraPrime" rel="nofollow">https://github.com/sriramvarun0636/HydraPrime): Systematic XAUUSD strategy in MQL5 (C++ derivative), developed and iterated solo over two years — OTE/Fair-Value-Gap structural entries with ATR-adaptive sizing and multi-layered drawdown circuit breakers. Backtested across 96M+ real ticks over a single two-year window (2024–2026) that included a strong gold bull run: 1.37 profit factor, 13.8% max drawdown, 252% net return. Not yet forward-tested live, so I hold that return number loosely until it survives a different regime.
What I'm looking for: Backend/infra/agent-systems roles at early-stage teams, pre-seed to Series A — founding engineer or engineer #2–5, wherever you're at. Standard interview loops work fine too, not just trial sprints.