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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.
Work arrangement
No
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