Generative AI Applications Engineer (Agents & RAG)

About the Role

You’ll turn mission needs into secure, reliable, and scalable GenAI applications with no model training required. This is a hands-on role across agentic workflows, RAG, prompt/policy design, LLM evaluation, and platform integration. You’ll own the end-to-end path from use case evaluation to production deployment and operational excellence, partnering with product, security, data, and SRE to ship features safely and at scale.

What You’ll Do

• Design & ship mission grade GenAI: Build agentic workflows and RAG systems tailored to mission data and environments; target low hallucination, tight p95 latency, and predictable cost.
• Agent frameworks & orchestration: Apply patterns from LangChain/LlamaIndex/Semantic Kernel; design task decomposition, tool use, guardrails, and recovery/fallback strategies.
• Platform integration (no model training): Implement with AWS Bedrock, Azure OpenAI, Google Vertex AI, Amazon Kendra, and managed services (e.g., Document AI, Gemini, Gemma).
• LLM selection & evaluation: Compare models for quality, safety, latency, cost; author/test prompts & policies; deploy with observability and safe rollback/fallback.
• RAG done right: Build retrieval pipelines & vector search (Pinecone, Weaviate, OpenSearch, pgvector, FAISS/Chroma); handle data prep, chunking, metadata, and IR-style evals (e.g., NDCG) to maximize signal to noise.
• Production rigor: Instrument metrics/logs/traces; run A/B experiments; maintain incident playbooks; and implement safety & compliance guardrails.
• SRE & FinOps for AI: Define SLIs/SLOs (quality/latency/safety/cost), run on call and postmortems, reduce MTTR; meter usage and optimize token/spend.
• Reusable platform components: Ship SDKs, CI/CD templates, Terraform/IaC modules, evaluation harnesses that accelerate multiple mission teams, not one-off projects.
• Operate in real-world constraints: Deliver into hybrid, restricted, or air-gapped environments with Zero Trust principles and audit-ready controls.

Requirements & Tech Stack

• Built and deployed a production GenAI application (chatbot, copilot, assistant, or enterprise AI).
• Hands-on with LLMs (GPT, Claude, Llama, Gemini, Mistral), via APIs or self-hosted.
• Designed and implemented RAG solutions using embeddings, vector databases, and semantic search for enterprise or mission data.
• Shipped AI applications with frameworks like LangChain, LangGraph, LlamaIndex, Semantic Kernel, DSPy, or similar.
• Strong Python development for building and integrating AI/ML applications.
• Owned AI solutions through full production life cycle, deployment, and operational support.
• Active U.S. citizenship.

Nice to Have

• Integration with leading cloud AI services or on-prem inference stacks.
• Background in LLM evaluation, prompt authoring/testing, A/B experimentation, and LLMOps.
• Responsible AI expertise (privacy, security, bias, transparency, human in the loop) and data governance.
• Experience implementing tools using agents for API integration and external data access.
• Containerization & orchestration (Docker, Kubernetes, VMware) and scripting/automation (Linux Bash, PowerShell).
• Prior work in regulated/secure environments (e.g., ATO, STIGs, Zero Trust) with fast shipping.
• Familiarity with NVIDIA AI Foundations, OpenAI ChatGPT, and AI-assisted dev tools (Cursor, Windsurf, Claude).
• Contributions to internal frameworks or open source; mentorship of engineers.
• Clear communication with engineers, PMs, and security/compliance stakeholders.