Job Title:
Principal AI Engineer (Contract)
Location:
McLean, VA or Richmond, VA (Hybrid)
Engagement Type:
7-Month Contract (with possible extension)
Overview
We are seeking a high-caliber Principal AI Engineer to accelerate the implementation of cutting-edge Agentic AI solutions. This is a hands-on builder role requiring a rare combination of deep Generative AI expertise, full-stack Python mastery, and strong AWS cloud architecture experience. You will play a critical role in transforming AI from experimental prototypes into production-grade autonomous systems that deliver real business value.
Key Responsibilities
Agentic Workflows:
Design, build, and deploy multi-agent systems using LLM orchestration frameworks (e.g., LangGraph, CrewAI) to automate complex cross-functional business processes, with a focus on measurable efficiency gains.
Production RAG Systems:
Develop and optimize high-performance Retrieval-Augmented Generation (RAG) pipelines using Amazon Bedrock and vector databases (e.g., OpenSearch, Pinecone), meeting defined latency and accuracy targets.
AI Integration:
Build scalable FastAPI backends that operationalize AI model outputs. Collaborate with frontend teams to support React-based AI interfaces, including real-time and streaming user experiences.
Responsible AI & Guardrails:
Implement safety mechanisms such as prompt controls, output filtering, bias evaluation, and content moderation to ensure compliance, security, and ethical AI use.
Engineering Excellence:
Establish robust AI evaluation frameworks (e.g., Ragas, DeepEval), observability systems (e.g., LangSmith, OpenTelemetry), and CI/CD pipelines for both code and prompt lifecycle management.
Required Experience
Software Engineering:
10+ years of professional experience
Python Development:
7+ years of backend development using Python
AI / Generative AI:
2+ years of hands-on experience implementing LLM-based solutions (e.g., Claude, GPT, Llama)
AWS Cloud Architecture:
5+ years designing and deploying cloud-native applications
Technical Skills
AI & ML:
Claude, GPT-series models, Hugging Face Transformers, PEFT, LangChain, LangGraph
Agent Systems:
Experience with autonomous agents, tool usage, function calling, and state management
Backend Development:
Python 3.10+, FastAPI, Pydantic, asynchronous programming
Cloud & Infrastructure:
Amazon Bedrock, SageMaker, AWS Lambda (serverless AI), RDS, pgvector
Observability & Evaluation:
Experience with AI monitoring, tracing, and evaluation frameworks
What Success Looks Like
Production-ready AI agents deployed with measurable business impact
Reliable, scalable RAG systems with strong performance benchmarks
Secure, compliant AI systems aligned with Responsible AI standards
Mature engineering practices applied to AI development lifecycle