AI Application Engineer

Summary

BigBear.ai provides AI-powered decision intelligence solutions for national security, supply chain management, and digital identity. The AI Application Engineer will develop and maintain full-stack AI applications and a config-driven decision-support platform integrating LLM-powered chat, structured data, and dashboards, while supporting backend APIs, databases, React interfaces, and application migration.

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

Skills

• All applicants must currently reside in the United States
• 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
• 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

Qualifications

Must Haves

• All applicants must currently reside in the United States
• 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