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Vice President of AI Engineering
- As VP of AI Engineering, you will own the technical foundation for AI across Komodo Health— designing the agent runtimes, orchestration layers, evaluation systems, and reference architectures that every product and engineering team builds on
- You’ll lead a team of engineers and data scientists scaling Marmot, our generative AI platform that transforms complex healthcare data into enterprise-grade insights, while establishing the standards, observability, and governance required to deliver AI in a regulated healthcare environment
- You will define reference architectures, establish engineering standards, influence build-versus-buy decisions, and act as a force multiplier across teams
- This requires deep hands-on expertise in LLM-based systems and a builder’s mindset as you don’t just set direction, you ship infrastructure
- Looking back on your first 12 months at Komodo Health, you will have accomplished..
- Established AI Engineering Standards: Defined reference architectures, shared evaluation frameworks, and governance models that give teams a reliable, auditable foundation to build on — not just a roadmap deck
- Hardened the AI Platform for Scale and Trust: Built evaluation, regression testing, observability, and lifecycle governance layers into Marmot — bringing the same rigor you’ve applied in other high-trust environments to healthcare
- Made AI-Native Development the Default: Teams across engineering actively use AI development tools (Claude Code, Copilot, etc.) for design, prototyping, refactoring, and system reasoning — because you modeled it yourself and built it into the workflow
- Secured Strategic Alignment: Partnered effectively with the C-Suite, Product, and Sales leadership to ensure AI infrastructure directly supports Komodo’s most critical commercial outcomes and platform efficiency goals
- These are the essential job duties you will be responsible for..
- AI Platform Architecture: Design and ship agent runtimes, orchestration layers, and shared evaluation systems that other teams depend on. Define reference architectures and engineering standards across the AI org
- LLM Systems Engineering: Own the integration of Large Language Models and NLP systems into production, engineering deterministic control planes around probabilistic models to ensure reliability at scale
- Trust and Compliance Engineering: Build auditability, regression testing, and lifecycle governance into every AI system. This is healthcare — reliability and data integrity are non-negotiable
- Build vs. Buy Decisions: Evaluate, select, and integrate AI tooling and vendor solutions with clear-eyed technical judgment. Know when to build internally and when to leverage external platforms
- Cross-Functional Force Multiplication: Partner with Engineering, Data Science, Product, and IT to embed AI capabilities into core data-linking, normalization, and analytical workflows — accelerating speed-to-insight across the organization
- Technical Advisory to Leadership: Serve as the primary technical advisor for senior stakeholders, translating complex AI systems concepts into clear, decision-ready strategy.
