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Senior Agentic AI Systems Engineer
Mission
Build dependable agentic AI systems that empower people rather than replace their judgment or authority across the entire mission lifecycle, from concept formulation and design through development, testing, verification, validation, flight operations, and science operations. Work with stakeholders to develop a shared understanding of where AI and machine learning are appropriate, which techniques best fit the need, what safeguards and evidence are required, and which decisions must remain with people.
Responsibilities
Work with mission operators, scientists, engineers, assurance personnel, and program leaders to understand their work, constraints, and decision authority. Build a shared problem definition and define how people and automated components will collaborate before selecting a technical solution.
Translate stakeholder needs and concepts of operations into requirements, system models, architectures, interfaces, technical budgets, and verification and validation plans. Identify assumptions, human decision points, and unresolved questions early.
Perform trade studies and reproducible experiments comparing conventional software, rules, planning, optimization, statistical learning, and generative models. Explain data needs, limitations, uncertainty, and lifecycle costs.
Write and maintain production code, automated tests, data pipelines, infrastructure definitions, and deployment automation. Build prototypes to resolve uncertainty, then develop accepted designs into maintainable operational capabilities.
Implement agents with explicit goals, decision mechanisms, persistent state, bounded actions, and recovery behavior. Apply agents to engineering tasks with traceable inputs, evaluated outputs, and accountable review.
Build long-running durable workflows, MCP servers and appropriate A2A, AG-UI, and A2UI integrations. Deploy models on cloud and on-premises infrastructure; configure model proxies for access, routing, usage attribution, and spending controls.
Stay current on research, engineering practices, model capabilities, and emerging interoperability standards. Test promising approaches against representative mission problems, and translate relevant changes into actionable recommendations for the program.
Implement safeguards and evaluation suites. Investigate failure modes, tune or adapt models when justified, and verify the quality and hardware impact of optimization choices.
Integrate and verify systems, prepare release and review evidence, and support deployment, training, operations, incident response, recovery, sustainment, and eventual replacement or retirement.
Support management with estimates, dependency and risk analysis, decision records, customer demonstrations, and clear explanations of progress and uncertainty. Review peer work and share reusable engineering practices.
Required Qualifications
Ownership of a substantial software-intensive capability through design, implementation, integration, and operational support, including diagnosis and resolution of a difficult failure.
Strong programming in Python or a comparable language, automated testing, API development, and deployment automation.
Hands-on use of a non-generative statistical or ML method, plus model or agent integration. Can evaluate alternatives and explain limitations and tradeoffs.
Demonstrated ability to stay current across AI/ML research and emerging standards, evaluate claims against primary evidence and experiments, and communicate relevant findings to technical and mission stakeholders.
Deployment experience in AWS or Google Cloud, containers, identity, networking, observability, and recovery. Can work with GPU-based hosting and evaluate fine-tuning results.
Experience with controlled engineering processes, traceability, verification evidence, and operational documentation. Communicates clearly, collaborates across employers, and escalates risks with evidence.
Preferred Experience
Model-based systems engineering and simulation; operation across both clouds and on-premises infrastructure; advanced fine-tuning, pruning, distillation, or reinforcement learning; production A2A or agent-interface integrations; Python and/or TypeScript.
Pay & Benefits
Salary is part of an overall compensation package and is determined within a range. This provides the opportunity for you to grow and develop within a role.
The salary range for this role is between $130,000 and $200,000, and your salary will depend on your skills, qualifications, experience and location.
You will also receive benefits including: Comprehensive medical, dental, and vision coverage with a health savings account, a 401k retirement plan with company match, and company paid life and disability insurance. We also offer permissive leave and holidays.
RA231 is an equal opportunity employer that is committed to inclusion and diversity. We take affirmative action to ensure equal opportunity for all applicants without regard to race, color, religion, sex, sexual orientation, gender identity, national origin, disability, Veteran status, or other legally protected characteristics.