Tech Stack

Description

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 teams to ship features safely and at scale.

Requirements

• End-to-end ownership of production systems from integration to deployment to observability to incident response
• Hands-on experience with LLMs, transformer-based apps, and RAG in production
• Strong Python experience
• Experience with vector search and retrieval technologies and grounding AI in enterprise/mission data
• U.S. Citizenship

Responsibilities

• Design and ship mission-grade GenAI applications with agentic workflows and RAG systems
• Apply agent frameworks orchestration patterns from LangChain/LlamaIndex/Semantic Kernel
• Implement platform integration with AWS Bedrock, Azure OpenAI, Google Vertex AI, and other managed services
• Conduct LLM selection and evaluation for quality, safety, latency, and cost
• Build retrieval pipelines with vector search technologies like Pinecone, Weaviate, OpenSearch, pgvector, FAISS/Chroma
• Instrument metrics/logs/traces, run A/B experiments, maintain incident playbooks, and implement safety compliance guardrails
• Define SLIs/SLOs for quality, latency, safety, and cost, run on-call and postmortems, and optimize token/spend
• Ship reusable platform components like SDKs, CI/CD templates, Terraform/IaC modules, and evaluation harnesses
• Operate in hybrid, restricted, or air-gapped environments with Zero Trust principles and audit-ready controls