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# Ai AgentsBazelCi/CdGithub ActionsLlmsMonorepo ToolingPython

- **Company:** Talkiatry Management Services, LLC
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $190,000.0 - $230,000.0
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Continuous Integration, Programming Tools, Software Reliability Testing, Search Technologies, Software Engineering, Large Language Models, Prompt Engineering, Model Validation, Data Analytics
- **Published:** September 10, 2026
- **Apply:** https://www.builtincolorado.com/job/staff-ai-enablement-engineer/11080553?handler=ApplyRedirect

## About the Role

* 8+ years in software engineering, with a track record of shipping production systems end-to-end
 * Hands-on experience building with LLMs and agentic systems - prompt engineering, context/tool design, evaluation, and orchestration
 * Real, production-level experience with modern AI coding assistants and agentic dev tools - not just awareness of them - and a clear point of view on when and how they add value
 * Working knowledge of applied ML fundamentals sufficient to help move a heuristic system toward a proper ML-based approach (features, evaluation, model selection, collaborating with/acting as a data scientist)
 * Demonstrated ability to drive technical adoption across an engineering organization without direct authority - through credibility, clear communication, and working software, not mandates
 * Strong product instincts for internal tooling: you talk to your "users" (fellow engineers), prioritize ruthlessly, and ship iteratively
 * Excellent written and verbal communication; comfortable presenting roadmap, tradeoffs, and results to engineering leadership
 * Prior experience in a staff/principal-level IC role, ideally with cross-team or platform scope

Nice to have:

 * Experience building internal AI enablement programs, developer-experience platforms, or lightweight AI governance frameworks
 * Experience in healthcare, healthtech, or another regulated industry
 * Familiarity with vector search / RAG, model evaluation frameworks, and observability for LLM-based systems
 * Experience with conversational AI / virtual assistant products

## Description

* Act as engineering's subject-matter expert on generative AI, agentic systems, and AI-assisted software development - tracking the landscape closely enough to separate durable capability from hype
 * Diagnose team-level workflows and pain points by meeting with every engineering team, translating findings into a prioritized backlog of AI-enabled solutions
 * Own the AI enablement roadmap end-to-end - propose it, get buy-in from engineering leadership, execute against it, and report on impact
 * Evaluate and pilot AI coding assistants and agentic tools, driving adoption decisions grounded in evidence rather than hype, calibrated to our team's size, stack, and current AI fluency
 * Design, build, and operate a network of specialized automation agents targeting high-friction engineering and operational workflows, on a shared architecture that lets new agents be added quickly and reliably
 * Instrument and measure the impact of each automation you ship (time saved, quality improvement, adoption) and iterate based on real usage
 * Partner with teams across the business to modernize existing rules-based or heuristic systems into more data-driven, ML-informed approaches, working closely with data and clinical stakeholders where relevant
 * Define lightweight adoption frameworks and guardrails (quality, security, cost, and responsible-use considerations) so new tools and agents can be rolled out safely and repeatably
 * Champion AI/automation best practices across engineering through documentation, demos, brown bags, and hands-on pairing with teams
 * Make the tradeoffs of any given AI approach (build vs. buy, cost, latency, quality, risk) legible to both engineers and leadership, Lead Root's AI Enablement Squad by defining AI-native ways of working, guiding cross-team adoption, connecting user workflows to AI Platform strategy, and turning experiments into reusable products and capabilities. Set technical direction, lead major initiatives, align senior stakeholders, establish adoption measures, promote responsible AI use, and mentor engineers and technical leaders across the organization.


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