Strategy Lead, AI- Operations/MedOps

What We Are Looking For

We care more about trajectory than tenure. The person who thrives here usually looks like this:

• Engineering-minded. A degree in computer science, engineering, math, or another quantitative field, or equivalent depth you can demonstrate.
• Data-first. Experience as a data scientist, analytics engineer, or analyst who builds, not just reports.
• Early in your career and moving fast. Often 2–5 years of experience, with a clear record of shipping more than your title required.
• Pulled toward product and the business. You want to own outcomes, not just tickets. You understand the business math behind what you build.
• Genuinely obsessed with AI. You build things on nights and weekends, trade links with friends, and can't stop thinking about where a new tool could apply.
• A thought partner, not a yes-person. You'll push back on our priorities when the data says we're wrong.

Must Have

• AI-native builder with a real technical foundation. You've personally built AI applications end to end, not managed a vendor deployment or written a slide deck for engineering. You're fluent in Python and SQL and comfortable with the data layer underneath: warehouses, pipelines, and data modeling. Thin wrappers around a chat model are not the profile we're looking for.
• LLM application depth. You can walk through model selection, retrieval design, evaluation, and failure modes, not just prompts. You've built retrieval (RAG) systems, designed real evaluation harnesses, and shipped workflows with human-in-the-loop checkpoints. You know when to use Claude, OpenAI, or open models, and why.
• Reliable, secure systems. You keep deterministic logic in code and use the model where judgment is needed. You think about where data lives, who can read and write it, how PII is handled, and how output gets audited before anything touches production.
• Analytical rigor. You can set up the measurement framework for a function, not just query someone else's. You design experiments, work in SQL against a warehouse, think in cohorts and segments, and can prove that something you built actually worked.
• Unstructured data experience. You've turned messy text, notes, or recordings into findings people trusted, and you can explain how you validated the output.
• Integration fluency. You can connect LLMs and automation to live company data: practice-management or EHR-like systems, CRM, data warehouses, and communication tools. You read API docs, debug integrations, and build multi-step workflows (in code or in tools like n8n, Make, or Zapier) without waiting on engineering.
• Product mindset. You ship products, not just automations. You do discovery with the people who'll use the tool, scope with trade-offs, measure adoption, and iterate on evidence.
• Ownership. Give you a goal and you'll find the path. You've worked where the roadmap was fuzzy, and you figured out what mattered most and drove it to done.

Strongly Preferred

• Agentic engineering. You've built agentic workflows that call tools, make decisions, and hand off to a human when they should. You bring real engineering discipline to AI-assisted building: tests, code review, and version control, not vibe coding.
• Custom MCP development. You've built or extended Model Context Protocol servers that plug LLMs into external tools and data.
• Service or healthcare operations exposure. You've worked in or built for a service business, ideally multi-location, and understand utilization, scheduling, and workforce planning. Not required; we'll teach the domain.
• High-performance environments. Background in a high-growth startup, product team, top-tier consulting, or banking.
• Healthcare data and AI governance awareness. You think carefully about medical data privacy, clinical accuracy, and when a human needs to review AI output. As a pet health brand, trust matters a lot to us.
• Prompt engineering discipline. You keep prompt libraries, version your system prompts, and run evals. Beyond using an LLM well, you build reliable systems on top of one.

What This Role Is Not

• Not a strategy-only role. You'll build and ship, not just advise. Expect roughly half your time with leaders and teams and half building.
• Not a dashboard-only analyst role. Analysis is the starting point; making it actionable with AI is the job.
• Not a pure software engineering role. You won't own production applications alone. Our engineering team partners with you to scale what you prove out.
• Not a clinical role. Our medical leaders own clinical standards and accuracy. You partner with them.

Why Petfolk

• Be part of a cohort of AI Leads building something new across the whole company.
• Full autonomy and real ownership, reporting directly to the functional VP
• A company that's genuinely investing in AI, not just talking about it.
• Work that connects to something real. Better marketing means more pet parents find better care for their animals.

Compensation:

$150,000 to $180,000 Annual Base

Apply with a portfolio of things you've built.

What you've built matters more to us