AI Engineer - Agentic Workflows

Cognitive Space builds next-generation AI systems that help “supercharge satellite operations” through its CNTIENT platform. We are seeking an engineer who thrives in a dynamic, fast-paced environment and enjoys taking ideas from theory into production deployments. 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.

Washington, DC area, Houston – hybrid in the office 2–3 times per week.

• 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.

• Bachelor’s/Master's/Ph.D. in a relevant field:
  Computer Science, Engineering, Applied Math, Statistics, etc.
• 1–5+ 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, Cloud Watch), 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/Tensor Flow familiarity; Lang Chain/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.

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

$150,000 - $205,000

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.