AI Engineer

What You'll Do

• 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

Basic qualifications

• 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

Nice to Have

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

Requirements

• 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

Perks

• Work from anywhere in the US

• Competitive pay

• Contribution toward health benefits

• High-visibility federal projects with real impact

• Small team where your ideas actually ship

• Generous exposure to the latest AI tooling and models

Get to know us

We're a small, creative, highly technical team. Our heroes are the scrappy folks who dare to dream and do great things. We care more about doing the right thing than taking shortcuts. We finish projects. We surprise our clients with how much we genuinely care about their success. We're selective about our partners — and our people. We don't say "human resource" because you're not a resource. You're a teammate, and we'll treat you like one.

What you should expect from us

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