8+ years of software engineering experience, with 1+ year on LLM applications or AI/ML systems in production.
Strong proficiency in Python and experience building AI/ML pipelines.
Hands-on experience with LLM orchestration frameworks like LangChain or AutoGen.
Deep understanding of RAG architectures and vector databases.
Experience integrating APIs from model providers and tuning prompts for reliability.
Strong fundamentals in software engineering, including clean architecture and observability.
Responsibilities
Architect and build multi-agent systems for document analysis and procurement automation.
Design agent orchestration using frameworks like LangGraph or custom solutions.
Develop capabilities for agents to decompose complex acquisition problems.
Build human-in-the-loop approval gates for automated workflows.
Design and optimize RAG pipelines grounded in authoritative procurement data.
Implement advanced retrieval strategies across diverse document corpora.
Select and integrate appropriate LLMs for specific tasks, balancing multiple constraints.
Benefits
Collaborative work environment focused on technical feedback and team elevation.
Opportunities to shape the implementation of agentic AI in a SaaS setting.
Full Job Description
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
