Note: This is a Hybrid Role - 3 days per week in our office in McLean, VA

What You’ll Do

Agent Design & Development

• Architect and build multi-agent systems and autonomous workflows for document analysis, requirement extraction, RFP response generation, and procurement pipeline automation.
• Design and implement agentic orchestration using frameworks such as LangGraph, AutoGen, CrewAI, or custom-built solutions tailored to Rohirrim’s platform requirements.
• Develop tool-using, reasoning, and planning capabilities that enable agents to decompose complex acquisition problems into executable subtasks.
• Build robust human-in-the-loop and approval gates for high-stakes decision points within automated workflows.

Graph RAG & Document Intelligence

• Design and optimize Retrieval-Augmented Generation (RAG) pipelines to ground agent outputs in authoritative procurement data, regulations (FAR/DFARS), past performance records, and institutional knowledge.
• Implement advanced retrieval strategies including hybrid search, re-ranking, and context-aware chunking across large, heterogeneous document corpora.
• Develop document parsing and structuring pipelines for RFPs, SOWs, PWS, solicitations, and other complex government acquisition artifacts.

LLM Integration & Evaluation

• Select and integrate appropriate LLMs (commercial and open-source) for specific agent tasks, balancing capability, latency, cost, and security requirements.
• Build evaluation frameworks to systematically test agent accuracy, hallucination rates, and task completion against procurement-specific benchmarks.
• Implement prompt engineering best practices, including structured outputs, chain-of-thought reasoning, and few-shot prompting optimized for acquisition workflows.

Platform & Infrastructure

• Collaborate with platform engineers to deploy agents in production with observability, logging, tracing, and graceful failure handling.
• Contribute to shared tooling, internal SDKs, and reusable agent components that accelerate development across the engineering team.
• Participate in architecture reviews, code reviews, and technical planning sessions with a focus on scalability and maintainability.

What You’ll Bring

Required

• 8+ years of software engineering experience, with at least 1 year focused on LLM applications, AI agents, or applied ML systems in production.
• Strong Python proficiency; experience building production-grade AI/ML pipelines.
• Hands-on experience with LLM orchestration frameworks (LangChain, LangGraph, AutoGen or equivalent).
• Deep understanding of RAG architectures, vector databases (Pinecone, Weaviate, pgvector or similar), and embedding models.
• Experience integrating APIs from frontier model providers and tuning prompts for structured and reliable results
• Strong software engineering fundamentals: clean architecture, testing, observability, and version control best practices.

Plusses

• Experience building AI products for regulated enterprise environments.
• Familiarity with federal acquisition regulations (FAR, DFARS) or proposal management processes.
• Experience with fine-tuning or instruction-tuning open-source LLMs (LLaMA, Mistral, etc.).
• Knowledge of secure-by-design principles and air-gapped or FedRAMP-compliant deployment environments.
• Active Secret or TS/SCI security clearance (or ability and eligibility to obtain).
• Experience with cloud infrastructure on AWS, Azure, or GCP; containerization with Docker/Kubernetes.

Success Profile

• A collaborative IC that is excellent at giving and receiving technical feedback, communicating trade-offs clearly, and elevating teammates.
• Ability to help define how agentic AI gets implemented in a SaaS company