Job Description
- Job Description:
- Define and build Vida's organizational approach to AI-augmented engineering—turning individual tool usage into a shared, scalable capability.
- Build the institutional knowledge systems that make Vida's architecture, domain context and golden path patterns accessible to both engineers and AI tools.
- Establish technical controls and governance for AI tool usage: approved tools, PHI-safe workflows, prompt guardrails and vendor evaluation processes.
- Own CI/CD reliability and velocity—targeting sub-5-minute median CI with automated preview environments, flake reduction and smart test selection.
- Build paved-road developer workflows: one-command local setup, service templates, repo standards and golden path generators.
- Own core GCP platform systems—compute, networking primitives, deployment automation and infrastructure as code.
- Define and track the metrics that show whether enablement is working: PR cycle time, CI duration, change failure rate, time-to-first-PR for new engineers.
- Hire and grow a team of 1–2 engineers as impact proves out.
- Requirements:
- Bachelors Degree at a minimum.
- 8+ years in engineering productivity, developer tooling, platform engineering or a related area—you've built things that made engineering teams measurably faster.
- Hands-on experience with AI coding tools (Claude Code, Codex, or similar)—you understand what they're good at, where they fail and how to make them better at the org level.
- Working knowledge of LLMs: context windows, prompting strategies and retrieval-augmented generation—you know how to structure information so AI tools use it effectively.
- Strong Python; comfortable with CI/CD systems, infrastructure-as-code tooling and cloud infrastructure on GCP.
- Experience in a regulated or security-heavy environment—you know how to ship with real constraints, not around them.
- You build internal platforms engineers actually adopt—because you pair tooling with standards, training and feedback loops.
Benefits:
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