We’re building debugging intelligence for modern software teams.
BetterBugs captures rich technical context at the moment a bug occurs and turns it into actionable insights for developers — not screenshots, not back-and-forth.
Our next leap: AI that understands bugs, not just describes them.
Senior AI / LLM Engineer (Python) : Build production LLM systems that understand real-world bug context (logs, stack traces, network, user actions).
Design agent workflows for root-cause analysis, bug triaging, and debugging assistance.
Implement RAG pipelines grounded in codebases, historical bugs, and execution data.
Own prompting, evaluation, reliability, and observability of AI outputs.
Build and operate Python services (FastAPI) powering AI features in production.
Strong Python, hands-on LLM/agent frameworks, Docker; bonus for dev tooling or observability experience.
Thanks for the suggestion — I’m happy to schedule a short intro call.
I’ve selected *13:00* as the preferred time.
For context, I’m a senior backend/systems engineer with strong Python experience, currently focusing on building production LLM-powered systems for debugging, incident analysis, and developer tooling. I’m particularly interested in BetterBugs’ approach to understanding bugs through real execution context (logs, traces, user actions), rather than surface-level descriptions.
Looking forward to the conversation and learning more about your vision and current needs.
We’re building debugging intelligence for modern software teams. BetterBugs captures rich technical context at the moment a bug occurs and turns it into actionable insights for developers — not screenshots, not back-and-forth.
Our next leap: AI that understands bugs, not just describes them.
Senior AI / LLM Engineer (Python) : Build production LLM systems that understand real-world bug context (logs, stack traces, network, user actions). Design agent workflows for root-cause analysis, bug triaging, and debugging assistance. Implement RAG pipelines grounded in codebases, historical bugs, and execution data. Own prompting, evaluation, reliability, and observability of AI outputs. Build and operate Python services (FastAPI) powering AI features in production. Strong Python, hands-on LLM/agent frameworks, Docker; bonus for dev tooling or observability experience.