Overview You will play a critical role in designing and shipping production-grade, tool-using, multi-agent LLM systems that can coordinate specialized components (e.g., planning, retrieval, decisioning, and execution) to complete complex, multi-step workflows in operational environments. This role is for a hands-on builder who can translate ambiguous workflow requirements into reliable multi-agent behavior, robust tool integrations, and measurable outcomes. Experience deploying and optimizing LLM solutions, including open-source model deployments and work in controlled or mission environments, is strongly valued.
Location Washington, DC area; Houston, TX - hybrid in office 2-3 times a week
What you will be doing - Design and build LLM-based agents that can plan and execute multi-step workflows (task decomposition, state management, memory, and controlled autonomy).
- Implement and maintain a robust tool interface layer (tool schemas/contracts, structured I/O, validation, retries, idempotency, and safe execution boundaries).
- Integrate agents with internal and external systems: APIs, databases, queues, and operational services, ensuring reliable action-taking and traceability.
- Develop evaluation and regression testing for agent behavior (scenario suites, golden traces, automated quality gates) to reduce drift and ensure predictable performance.
- Establish observability for agent runs (tracing, failure analysis, latency/cost monitoring, tool-call success rates) and drive continuous improvements.
- Containerize and deploy agent services and decision-making capabilities for production and edge environments, as applicable.
- Collaborate with cross-functional teams to identify and prioritize agentic automation opportunities tied to key milestones and requirements.
- Implement safety and governance controls (permissions, policy checks, audit logs, and appropriate handling of sensitive data) aligned to operational constraints.
What you will need - US Citizenship
- Active TS/SCI clearance preferred or must be able to obtain and maintain a TS/SCI Clearance.
- Preferred Security+ Certification
- Bachelor's/Master's/Ph.D. in a relevant field: Computer Science, Engineering, Applied Math, Statistics, etc
- 2-5+ years of professional experience as an AI Engineer, ML Engineer, Software Engineer (Applied AI), Applied Scientist, or similar role
- Strong programming skills in Python; experience with production services, APIs, and containerization (Docker/Kubernetes) is strongly preferred.
- Experience deploying and operating workloads on AWS (e.g., IAM, VPC, EC2, EKS/ECS, Lambda, S3, CloudWatch), including security, monitoring, and cost-aware design
- Hands-on experience building LLM applications in production, including several of:
- Experience with ML/LLM frameworks and ecosystems (e.g., PyTorch/TensorFlow familiarity; LangChain/Strands/Bedrock familiarity; vector databases/search) consistent with an applied engineering role.
- Strong debugging and analytical skills: ability to diagnose failures across model behavior, tool execution, and system integration.
- Ability to convey complex technical behavior and trade-offs in a clear, practical manner.
- Prefer experience in space/satellite/aerospace domains, mission operations, systems engineering, or adjacent operational environments.
- Prefer experience with offline/online evaluation methodologies and building reusable test harnesses for AI behavior.
Benefits One of the most interesting aspects of working at a startup company is gaining equity, which means our success is your success. In addition to equity in the form of options, we also offer:
- Flexible Time-Off policy and company holidays
- Cost-effective health care, dental, and vision with company contributions
- 401k matching plan with company match
- Life insurance
- Short-term and long-term disability
Salary$160,000 - $195,000
We value job-related knowledge and skills, education, and experience. That's why we will determine your actual level and base salary on a case-by-case basis, considering these factors. We believe this will ensure fair and competitive compensation for you.
