# AI Enablement Engineer

**Company:** [Sprinter Health](https://hotfix.jobs/companies/sprinter-health)
**Location:** San Francisco, CA
**Role:** DevOps / SRE
**Salary:** $180k – $260k/yr
**Experience:** 7+ years
**Skills:** Python, TypeScript, LLMs, RAG, AI Agents, Prompt Engineering, Evaluation Frameworks, CI/CD, HIPAA, mcp servers
**Posted:** 2026-07-20

> Build and enable company-wide AI adoption by creating agents, workflows, prompt libraries, evaluation frameworks, and training programs. Partner with engineering, clinical, operations and other teams to identify opportunities, deliver production AI tools, and ensure safe, measurable impact in a regulated healthcare environment.

## Job Description

## What you will do
- Help define and drive Sprinter’s AI enablement strategy across engineering, operations, clinical, data, finance, and other functions
- Embed with teams to understand their workflows, identify high-leverage AI use cases, and translate business needs into working technical solutions
- Build bespoke agents, background workflows, internal tools, and automations that solve real operational, clinical, and engineering problems
- Create reusable playbooks, prompt libraries, skill libraries, workflow templates, and reference architectures that teams can self-serve
- Stand up shared context and knowledge systems that help AI tools ground answers in Sprinter’s data, documentation, codebases, and organizational context
- Evaluate, configure, and recommend AI tools, making practical build-versus-buy decisions based on team needs, safety, scalability, and cost
- Tune AI coding assistants and agentic workflows to Sprinter’s codebases, conventions, and development practices
- Build evaluation sets, benchmarks, and review patterns that help teams separate useful AI outputs from convincing-but-wrong ones
- Establish safe, repeatable deployment patterns for AI-built applications, internal tools, models, workflows, and data tables
- Partner with SRE, IT, Security, Legal, and clinical stakeholders on tool approval, deployment, access patterns, and PHI-safe guardrails
- Run recurring office hours, trainings, hackathons, and hands-on enablement sessions that build AI fluency across the company
- Measure AI adoption, productivity gains, quality improvements, and operational impact in ways that go beyond usage or token counts
- Communicate AI strategy, adoption progress, risks, and opportunities to individual contributors, managers, and executive leadership
- Help non-experts move quickly while ensuring patient safety, privacy, and quality are built into the workflow from the start

## What you have done
- Built production-quality software in Python, TypeScript, or similar languages
- Worked hands-on with LLMs, AI assistants, agents, tool calling, structured outputs, RAG, or other applied AI patterns
- Built internal tools, automations, workflows, developer productivity tooling, AI-enabled applications, or agentic systems
- Designed practical evaluations, benchmarks, or QA processes for AI workflows or software systems
- Worked with CI/CD, testing, deployment pipelines, or production release processes
- Gathered requirements from non-technical stakeholders and translated them into scoped, working technical solutions
- Enabled teams through documentation, training, office hours, workshops, hackathons, or reusable templates
- Used AI coding assistants such as Claude Code, Cursor, or similar tools as part of your day-to-day development workflow
- Made practical tradeoffs between speed, safety, usability, maintainability, and cost
- Communicated technical concepts clearly to audiences ranging from engineers to executives
- Operated in fast-moving, ambiguous environments where the path was not already defined

## What gives you an edge
- Operated at Senior, Staff, or equivalent scope, driving technical decisions across multiple teams
- Built internal AI platforms, agent frameworks, evaluation systems, workflow automation platforms, or developer productivity tooling
- Helped a company or team adopt AI tools in a measurable, repeatable way
- Experience standing up a centralized prompt library, skill library, workflow library, or knowledge/context hub
- Worked with MCP servers, internal tool integrations, RAG systems, or AI agents connected to real business systems
- Experience with healthcare data, PHI, HIPAA-aware workflows, or regulated environments
- Partnered with security, IT, legal, compliance, or clinical teams to approve and deploy AI tools safely
- Public or internal track record of teaching, writing, workshops, talks, or training that made complex technical ideas accessible
- Worked in a startup or high-growth environment where enablement, velocity, and practical judgment mattered

## What makes you successful
- You are a force multiplier and measure success by what the whole organization can now build with AI
- Meet teams where they are, ship the first working example, and turn it into a template others can reuse
- Reach for the simplest tool that safely solves the workflow
- Build for safety from the start through guardrails, evaluations, review patterns, and PHI-aware defaults
- Back adoption claims with evidence, including evals, benchmarks, productivity metrics, and quality improvements
- Teach as well as you build
- Can make AI make sense to an engineer, an operations lead, a clinician, and an executive
- Help people move faster without making patient safety or privacy someone else’s problem
- Create systems that make good AI usage easier and risky AI usage harder

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