Job Title: Principal AI Engineer
Location: McLean (open to Richmond)
Duration: 7 months (chance of extension)
Job Description:
We are seeking a high-caliber Principal AI Engineer for a contract engagement to accelerate the implementation of Agentic AI solutions. This is a ""builder"" role requiring a rare blend of deep Generative AI expertise, full-stack Python mastery, and AWS cloud
architecture. You will be responsible for moving AI from experimental prototypes to production-grade autonomous agents.
Experience Requirements:
Total Experience:
10+ years in Software Engineering.
Python Mastery:
7+ years of professional backend development.
AI/GenAI:
2+ years of hands-on implementation with LLMs (Claude, GPT, Llama).
AWS:
5+ years architecting cloud-native applications.
Key Responsibilities:
Agentic Workflows:
Design and deploy multi-agent systems using LLM orchestration frameworks (e.g., LangGraph, CrewAI) to automate complex cross-functional business processes, targeting measurable efficiency gains.
Production RAG:
Build and optimize high-performance Retrieval-Augmented Generation pipelines using Amazon Bedrock and vector databases (e.g., OpenSearch, Pinecone), with clear latency and accuracy targets.
AI Integration:
Develop robust FastAPI backends that make model outputs actionable for end-users. Collaborate with frontend engineers on React-based AI interfaces and streaming UI components.
Responsible AI & Guardrails:
Implement prompt safety mechanisms, output filtering, bias evaluation, and content moderation to ensure production LLM systems meet security and compliance standards.
Engineering Excellence:
Implement automated AI evaluation frameworks (e.g., Ragas, DeepEval), observability tooling (e.g., LangSmith, OpenTelemetry), and CI/CD pipelines for LLM prompts and code.
Technical Stack
AI:
Claude, GPT-series, Hugging Face Transformers, PEFT, and LLM orchestration frameworks (e.g., LangChain, LangGraph).
Agents:
Experience with autonomous tool-use, function calling, and state management.
Backend:
Python 3.10+, Pydantic, FastAPI, and asynchronous programming.
Cloud:
Amazon Bedrock, SageMaker, Lambda (Serverless AI), and RDS/pgvector.
Observability:
Experience with AI-specific monitoring, tracing, and evaluation tooling.