AI Enablement Engineer

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