Artificial Intelligence Engineer

We build agentic AI systems for institutional investors, powered by two engines: OmniContext™, our hybrid context engine, and SmartOrch™, our agentic orchestration engine.

Building and deploying AI applications

• Multi-agent workflows in LangGraph, LangChain and Google ADK — routing, delegation, durable execution, human-in-the-loop
• Hybrid retrieval: knowledge graph (Neo4j/Cypher) + vector (pgvector, Qdrant) + SQL, with query routing and reranking
• Gemini, OpenAI, Azure OpenAI and Anthropic, with model-agnostic routing and fallback
• Agent harness — tools, MCP, guardrails, structured outputs, context and token budgeting
• Eval infrastructure — golden datasets, regression suites, grounding and hallucination checks
• Production tracing: model, prompt version, retrieved span, tool call, approver

Software engineering fundamentals

• Python (FastAPI, Pydantic, asyncio) and Node.js/TypeScript services; React/Next.js front-ends
• Postgres and Firestore modelling; document ingestion, entity resolution, schema-drift detection
• Docker, Kubernetes, Terraform, CI/CD on GCP, Azure or AWS
• SSO/RBAC, private networking, secrets management, audit logging
• Deployment into client cloud, on-prem and restricted environments — including open-weight serving (vLLM, Ollama)

Orchestrating agents

• Decomposing work into tasks an agent can complete, with the context to make that likely
• Setting up tests and feedback loops for longer unsupervised runs
• Reviewing agent output critically — you own everything that ships under your name
• Building skills, tools and MCP servers so agents are useful on our codebase

Shaping the build

• Scoping ambiguous client problems into something shippable
• Taking a technical position and defending it, with nobody senior to defer to
• Knowing when a workflow doesn't need an agent

You

• 4+ years shipping production software, full stack in Python and TypeScript
• Built a RAG system and then fixed it; can talk about failure modes, chunking, reranking
• Production experience with an agent framework — not tutorials
• You write evals and have caught a regression before a user did
• Strong SQL; graph databases or able to pick them up fast
• Docker, Kubernetes, CI/CD and at least one major cloud
• Comfortable in front of a client, not just a codebase

Bonus: entity resolution

• text-to-SQL
• MCP/A2A
• Vertex AI or Azure OpenAI in production
• on-prem or regulated delivery
• financial services domain

We're hiring two engineers to expand the core team.