AI Application Engineer

Bigbear.ai•UNAVAILABLE, UNAVAILABLE

10d•Onsite

About The Position

BigBear.ai is seeking an AI Applications Engineer. In this position, you will develop and maintain AI applications and a config-driven decision-support platform that combines LLM-powered chat (RAG), structured data (MCP/PostgreSQL), and dashboards to support operational readiness and logistics workflows. You will own features end-to-end across backend APIs, database, and the modern React UI, with ongoing support for the existing Dash/Flask application during migration. All applicants must reside in the National Capital Region.

Requirements

3+ years full-stack development with strong Python and React/TypeScript
Must be able to obtain and maintain a security clearance
Hands-on LLM/RAG experience building production features with LangChain or LangChain4J (chains, retrievers, agents/tools, embeddings, vector stores)
Experience building and consuming REST APIs (FastAPI or similar)
Solid PostgreSQL skills: schema design, SQL, migrations (pgvector a plus)
Comfortable with Docker, local dev environments, and basic deployment workflows
Strong debugging, testing, and code review habits
Ability to work independently across backend, frontend, database, and AI layers
Proven comfort using AI coding assistants as a daily workflow—not as a substitute for understanding the codebase

Nice To Haves

Dash/Plotly, Flask, or legacy-to-modern UI migration experience
Keycloak/OAuth, Redis sessions
Nx monorepos, TanStack Router/Query, Tailwind CSS
MCP protocol, Ask Sage, or similar enterprise LLM platforms
Government/defense or regulated-environment experience

Responsibilities

Build and maintain full-stack features: FastAPI services, PostgreSQL schema/migrations, and React/TypeScript UI in an Nx monorepo
Design, implement, and maintain LLM/RAG pipelines using LangChain (Python) or LangChain4J (Java)—retrieval, embeddings, tool/agent orchestration, and prompt workflows
Integrate LLM providers (e.g. OpenAI, Anthropic, Ask Sage), document ingestion, semantic search, and chat/report features
Work with vector stores (e.g. PostgreSQL (pgvector), Weaviate), Redis, and Keycloak auth; implement role-based access and secure API patterns
Connect to external systems via MCP, REST/PostgREST, and related data adapters
Write and maintain tests (pytest, Vitest) and participate in CI/CD (Docker, GitHub Actions)
Debug production issues, improve reliability/performance, and keep dependencies and security patches current
Use AI-assisted development tools (e.g. Cursor) effectively: prompt well, review generated code critically, and ship production-quality changes at a steady pace