RCA Agent Enhancement Needed

I run a set of RCA (Root-Cause Analysis) agents that already serve production traffic, yet their behaviour is no longer where I want it to be. I’m looking for someone who can dig into the existing Python-based micro-services, profile what is slowing them down, and then refactor or extend the code so the agents respond faster, consume less memory, and remain rock-solid under peak load.

You will start by cloning the private Git repo, setting up the local environment, and producing a brief audit that highlights performance bottlenecks and any obvious design flaws. Once we’ve agreed on priorities, you’ll implement the improvements, write or update unit tests, and keep the work tracked through pull requests so I can review each change easily.

Deliverables:
• Optimised source code merged via PRs
• Expanded automated test suite with coverage report
• Updated README and concise change log summarising what was altered and why

Acceptance criteria:
• Measurable reduction in average response time and memory footprint
• No regression in the existing feature set (all tests green)
• Clear documentation that lets another developer pick up the agents without guesswork

If you’re comfortable working with Python, RESTful APIs, and common monitoring stacks such as Prometheus or ELK, you’ll feel at home in this project.