Vice President of AI Engineering, Komodo Health

• You're a Technical Visionary who brings 10+ years of experience leading technical organizations within B2B SaaS or high-scale data environments, with a proven track record of scaling revenue-critical platforms
• Proven track record designing and shipping foundational AI infrastructure including agent runtimes, orchestration layers, evaluation frameworks, and observability — that other engineering teams build on. You’ve engineered deterministic control planes around probabilistic models and know what it takes to make LLM-based systems reliable at scale
• Deep experience delivering AI systems in regulated, high-trust environments with built-in auditability, regression testing, and lifecycle governance. Healthcare, fintech, defense, govtech — the domain matters less than the rigor. You’ve written production-grade Python and integrated modern GenAI tooling into platforms where compliance and reliability are non-negotiable
• Demonstrated use of AI tools as force multipliers in your own engineering workflow whether that’s Claude Code, Copilot, or similar systems for design, prototyping, refactoring, and system reasoning. You don’t just build AI for others; you leverage it to move faster yourself
• Proven leadership building high-performing AI/ML teams with a hands-on, service-oriented approach. You’ve scaled engineering organizations in fast-moving environments and know how to build a culture where AI-native workflows are the default, not the exception

Desirable

• 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.