Senior AI Enablement Engineer

Jobtailor; San Francisco (CA); On-site; USD 150,000 - 210,000; Full time

Job summary

Sprinter is seeking an AI Enablement Lead to help define and drive our AI strategy across engineering, operations, clinical, data, finance, and other functions. You will embed with teams to translate business needs into working technical solutions.

You will build internal tools, bespoke agents, and reusable playbooks, establish safe deployment patterns, and enable fluency in AI across the company while protecting patient safety and data privacy.

Qualifications

• Built production-quality software in Python or TypeScript.

Responsibilities

• Define and drive AI enablement strategy across functions.

Skills

Python Programming

TypeScript Programming

LLM Application

Benchmark Design

QA Process Development

AI Workflow Evaluation

Internal Tool Automation

CI/CD Pipeline Management

Stakeholder Communication

Tools

Claude Code

Cursor

Deployment Pipelines

Developer Productivity Tools

AI Coding Assistants

Job description

• 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

Requirements

• 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

Core Competencies

Demonstrates expertise in AI enablement strategies, building internal tools, and automating workflows while ensuring safety and quality in clinical and operational contexts. Proficient in translating business needs into technical solutions and fostering AI fluency across diverse teams.

Highest-signal resume keywords

• Python Programming
• AI Assistant Development
• Internal Tool Automation
• CI/CD Pipeline Management
• Stakeholder Communication

ATS Optimization Keywords

Hard Skills

• TypeScript Programming
• LLM Application
• Benchmark Design
• QA Process Development
• AI Workflow Evaluation

Soft Skills

• Training Facilitation
• Documentation Skills
• Adaptability in Ambiguous Environments
• Clear Communication

Industry Keywords

• AI Enablement
• Operational Impact Measurement
• Patient Safety
• Privacy Compliance
• Workflow Automation

Tools & Technologies

• AI Coding Assistants
• Claude Code
• Cursor
• Deployment Pipelines
• Developer Productivity Tools