AI Full Stack Developer

The Opportunity: The candidate will be responsible for designing, building, and deploying end-to-end web applications that leverage Artificial Intelligence (AI) and Large Language Models (LLMs) within a secure Department of War (DoW) environment.

This requires standard full-stack development skills (frontend UI, backend APIs, and databases) combined with the ability to integrate modern AI pipelines.

Required: · Backend Development: Advanced proficiency in Python and API frameworks (e.g., FastAPI, Flask) to build the core logic and connect user interfaces to AI models. · Frontend Development: Experience building clean, intuitive user interfaces using modern frameworks (e.g., React, Vue) or AI-focused UI libraries (e.g., Streamlit) for chat interfaces and search dashboards. · Data & Database Architecture: Strong experience with relational databases (e.g., PostgreSQL) and Vector Databases (e.g., pgvector, Milvus, Chroma) to manage both application data and AI-searchable embeddings. · AI Data Integration (RAG): Hands-on experience designing and implementing Retrieval-Augmented Generation (RAG) workflows to allow the AI to securely reference and search internal documents. · DevOps & Infrastructure: Experience containerizing applications using Docker/Podman and utilizing version control (Git) to package applications for stable deployment. Desired: · Secure / Air-Gapped Deployment: Understanding of how to deploy containerized applications in restricted, disconnected, or IL5+ environments without relying on active external cloud connections. · Open-Source AI Ecosystem: Experience utilizing the Hugging Face library/hub to evaluate, download, and implement open-weight models (e.g., Llama 3, Mistral) locally. · Local Compute Optimization: Familiarity with model runtime tools (e.g., vLLM, llama.cpp) and quantization techniques (e.g., GGUF) to run large AI models efficiently on standard DoD hardware. · AI Orchestration Frameworks: Familiarity with frameworks like LangChain or LlamaIndex to accelerate the development of complex AI agent workflows. · AI Safety & Evaluation: Ability to implement system "guardrails" to prevent off-topic outputs and mitigate AI hallucinations, ensuring secure and accurate mission-support tools.