Full-Stack AI Engineer, Komodo Health

• Experience building production-grade AI systems or AI-powered applications
• Strong proficiency in Python
• Experience working with LLMs, prompt engineering, or agent-based architectures
• Familiarity with modern GenAI tooling and frameworks: vLLM, CrewAI, Strands, OpenAI / Chat Completions APIs
• Ability to integrate AI capabilities across backend services and product interfaces
• Experience designing evaluation frameworks, testing strategies, or monitoring systems for AI features
• Strong collaboration skills across engineering, product, and data teams
• Expectations of AI Use in this role (required):
• At Komodo, AI is core to how we build. Full-Stack AI Engineers are expected to actively experiment with new AI techniques, share learnings across teams, and contribute to evolving best practices for building reliable, scalable AI systems. You will play a key role in shaping Komodo's AI-first engineering culture
• Additional skills and experience we’d prioritize (nice to have)…

Desirable

• As a Full-Stack AI Engineer, you will design and deploy end-to-end AI solutions that power real products and internal tools. You'll work at the intersection of applied research, engineering, and product development — bringing modern AI techniques into scalable production systems
• You'll operate in a lean, high-leverage pod: a small team of Full-Stack AI Engineers paired closely with a PM who deeply understands the customer workflow — built to compress the discovery loop and ship things that actually matter
• You'll also collaborate with platform and data teams to build AI capabilities that transform how healthcare data is explored, understood, and operationalized
• Looking back on your first 12 months at Komodo Health, you will have accomplished…
• Shipped production AI features that improve the precision, usability, and intelligence of Komodo's platform
• Designed and deployed agent-based AI pipelines integrated into real customer-facing products
• Built internal AI productivity tools that accelerate engineering workflows across Komodo
• Prototyped and validated new AI approaches using emerging research and model capabilities
• Contributed reusable prompt templates, orchestration patterns, and AI system architecture
• Implemented monitoring, evaluation, and observability frameworks for deployed AI services
• Designing, building, and deploying end-to-end AI systems across the full intelligence stack — from data context and retrieval to agent orchestration, evals, versioning, and governance
• Developing agent pipelines, prompt chains, and orchestration frameworks for LLM-driven workflows
• Selecting the right AI technique (LLMs, classic ML, or hybrid approaches) for the problem at hand
• Collaborating with PMs, engineers, and data scientists to define requirements and deliver solutions
• Building scalable AI services with monitoring, evaluation, and deployment pipelines
• Contributing reusable patterns to Komodo's AI infrastructure and internal tooling ecosystem