AI Engineer

Design and build agentic systems — multi-step agents that plan, call tools, retrieve context, and take action with appropriate human-in-the-loop checkpoints

Build MCP servers and clients to securely expose client data, internal tools, and APIs to LLMs in a standardized, auditable way

Ship LLM-powered applications: copilots, document intelligence, search, summarization, and workflow automation

Design and maintain RAG pipelines — chunking, embeddings, vector stores, retrieval, reranking, and grounding

Integrate model APIs (OpenAI, Anthropic, Bedrock, Azure OpenAI, open-weight models) and pick the right model for the job based on quality, latency, and cost

Develop evals and observability for agents and AI features so we know what's working in production and what's regressing

Apply prompt engineering, structured outputs, function/tool calling, and guardrails to make agent behavior predictable

Write production Python backends and APIs that expose AI capabilities to web and mobile clients

Collaborate with engineers, designers, and product folks to scope what AI should (and shouldn't) do in a given product

Help shape responsible AI practices for federal use — privacy, security, auditability, and human oversight

5+ years of professional software engineering experience, with at least 1 year shipping LLM-based or AI-powered features to production

Hands-on experience designing or building agentic systems — tool calling, multi-step reasoning, planning loops, or agent orchestration (LangGraph, CrewAI, OpenAI Agents SDK, Claude tool use, or equivalent)

Working knowledge of the Model Context Protocol (MCP) — or demonstrated ability to pick it up quickly, plus familiarity with the broader landscape of agent/tool standards

Strong Python and experience building and deploying backend services and APIs (FastAPI, Flask, or similar)

Hands-on experience with at least one major LLM provider (OpenAI, Anthropic, Bedrock, Azure OpenAI, Vertex, or open-weight models via vLLM/Ollama)

Working knowledge of RAG: embeddings, vector databases (pgvector, Pinecone, Weaviate, Qdrant, or similar), and retrieval evaluation

Comfort with prompt engineering, structured outputs (JSON mode, schemas), and tool/function calling

Experience writing evals — even lightweight ones — for non-deterministic systems

Solid SQL and experience with relational and unstructured data

Familiarity with at least one cloud platform (AWS, Azure, or GCP)

Git, code review, and modern collaborative workflows

Strong written and verbal communication — you can explain AI tradeoffs to non-technical stakeholders

Experience authoring MCP servers for non-trivial systems (databases, internal APIs, document stores)

Experience with eval and observability platforms (Braintrust, LangSmith, Langfuse, Arize, or custom harnesses)

Multi-agent orchestration patterns and experience reasoning about agent failure modes

Fine-tuning, distillation, or LoRA experience where it actually moved the needle

Docker, Kubernetes, and CI/CD for AI workloads

TypeScript/Node for full-stack AI features

Streaming UIs (SSE, WebSockets) and token-level UX patterns

Experience with caching, prompt compression, and cost/latency optimization at scale

Background supporting federal or government clients

Awareness of NIST AI RMF, FedRAMP, or related responsible-AI frameworks

Must be a US Citizen or legal resident, able to work domestically

Must be able to attain a low-level security clearance

Must work from the United States

We will treat you fairly

We give you space to grow personally and professionally

We will hear your ideas even when we disagree

We will be honest about our challenges and equitable with our success

We will tell you the truth, even when it's difficult