Full Stack AI Engineer

• Location: Denver, CO (hybrid — Tue/Wed/Thu in office)
• Reports to: CEO

The role

We're hiring a senior full-stack engineer to build the next generation of Ombud's agentic AI platform. The work splits across two domains: building the agentic engine itself (LlamaIndex / agent orchestration / tool calling / evaluation pipelines) and the ML data engineering that supports it (embeddings, vector store operations, retrieval quality, RAG/CAG architectures).

These are high-output IC roles. You will ship production code, own systems end-to-end, and operate without a layer of engineering management between you and the product direction. You will work directly with the CEO on architectural decisions and directly with the platform engineer on production deployment. We are not hiring engineering managers and we are not hiring junior engineers.

What you'll own

• Ombuddy Native: our next-generation agentic platform replacing the existing Chrome extension. Production agent orchestration, tool design, multi-step reasoning workflows.
• RAG and CAG architecture: embeddings, retrieval, re-ranking, caching strategies. We use LlamaIndex and Qdrant today; we will evolve the stack as needed.
• LLM integration with Anthropic Claude (primary), with multi-model routing where it makes sense.
• Evaluation pipelines: how we measure response quality, regression-test prompts, and ship LLM-dependent features with confidence.
• Self-service infrastructure: customer onboarding flows, content ingestion automation, in-product setup experiences.
• Full-stack feature delivery across Python (primary backend), Node.js (legacy services), React (frontend), PostgreSQL, and Elasticsearch.
• Production operations: own your features through deployment, monitoring, and customer-facing incidents.
• Code review and technical mentorship within a small, senior engineering team.

Must-haves

• 6+ years of professional full-stack software engineering experience, with demonstrated production system ownership.
• Deep Python fluency. JavaScript / TypeScript / React competence.
• Hands-on production experience integrating LLMs into product (Anthropic, OpenAI, Google) — not academic, not prototype work, but features that customers use.
• Working knowledge of RAG architectures, embeddings, vector databases, and the trade-offs between retrieval and context-caching approaches.
• Fluency with Claude Code or similar AI-augmented development workflows. We expect our engineers to use AI as a force multiplier on their own output.