[Hiring] Staff AI Enablement Engineer @Talkiatry

Role Description

Talkiatry is hiring a Staff AI Enablement Engineer to define and drive how generative AI and automation are used across our engineering organization. This is a builder's role first: you'll design and ship working automations and agents, not just write recommendations. It's also a force-multiplier role: your job is to make every engineering team faster and more capable by embedding proven AI practices, tools, and infrastructure into their day-to-day work.

You'll work directly with the Engineering and Product leaders and teams to understand their workflows, tooling gaps, and pain points — then design automation, agents, and tooling that measurably reduce toil. You'll also own our engineering-wide point of view on AI coding assistants and agentic developer tools, evaluating what's real versus hype and driving thoughtful, phased adoption suited to our team's current maturity. Beyond developer-facing enablement, you'll have the opportunity to apply AI and ML directly to core business workflows, modernizing existing rules-based systems with more data-driven approaches.

You will:

• 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.

Qualifications

• 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.

Requirements

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

Benefits

• Meaningful mission — your work directly helps a lean engineering team spend more time on what moves the business.
• Shape how an entire engineering org builds with AI — real autonomy and direct access to engineering leadership.
• A mandate to build things that matter, not produce slideware.
• Impact that spans developer productivity, operations, and the broader patient experience.
• Competitive compensation, benefits, and remote flexibility.