AI Engineer

• Enablement & coaching — work directly with engineers through 1:1s, pair programming, workshops, office hours, and cohorts to raise their effectiveness with AI-assisted development: prompting, workflow design, and model/tool selection. Identify under-adoption and lift teams up to the standard.
• Best-practice propagation — capture what works (and what fails) in one team and systematically transfer it: living best-practice guides, prompt libraries, troubleshooting playbooks, and an internal AI knowledge base and skills registry.
• Scale through champions — stand up and support a network of embedded AI champions so enablement scales past a single person.
• Unblocking & feedback loop — be the go-to when engineers get stuck with AI tooling. Proactively tour teams to surface blockers early, aggregate them into patterns, and escalate systemic issues to leadership.
• Internal integrations & tooling — build internal AI integrations and reusable tooling hands-on (Node.js/TypeScript, MCP) — from prototype to production independently, including auth, access control, security review, and CI/CD.
• Evaluation & scouting — run structured evaluations of AI tools, models, and internal integrations; monitor the fast-moving landscape (Claude Code, Cursor, Copilot/Codex, new model releases) and deliver curated, actionable recommendations.
• Measurement & impact — define and track adoption and productivity metrics by team, tool, and individual. Build dashboards on real usage and output — adoption/active-usage rate, assistant acceptance rate, PR throughput and cycle time — with a quality guardrail (change failure rate) so speed never comes at the cost of quality. Reference frameworks: DX Core 4, DORA.
• Cross-functional partnering — partner with security/compliance on safe-by-design guardrails, and with GTM to translate field needs into internal enablement and feed adoption patterns back.

Senior AI Enablement Engineer

Machine Learning Engineer

• Create ML models from scratch or improve existing models.
• Collaborate with the engineering team, data scientists, and product managers on production models.
• Develop experimentation roadmap.
• Set up a reproducible experimentation environment and maintain experimentation pipelines.
• Monitor and maintain ML models in production to ensure optimal performance.
• Write clear and comprehensive documentation for ML models, processes, and pipelines.
• Stay updated with the latest developments in ML and AI and propose innovative solutions.

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Senior AI Enablement Engineer

Senior ML Engineer, LLMs, AWS