HAI - DoD MLOps Software Engineer

Charles River Analytics, a GRVTY company, creates solutions and technology to tackle the world’s most challenging problems. Our team of technological entrepreneurs works together to push at the forefront of enhanced AI, robotics, smart sensing, and human-centered computing. The resulting research and development help to continuously advance government programs and discover new possibilities in the commercial marketplace. At Charles River, we take great pride in our success at attracting and retaining the most talented and creative problem-solvers in our field. Now as part of GRVTY, we offer the same trusted capabilities with increased organizational depth and expanded capacity across mission-critical national security domains. Are you ready to accelerate our mission-focused innovations? We’d love to hear from you!

What You'll be Owning:

We have an exciting opportunity for an MLOps Software Engineer to join a customer on-site as an embedded technical analyst. In this role, you will be responsible for customizing AI agent training configurations and tailoring agent-based simulation backends to support military wargaming and analysis. Your responsibilities will include helping customers define analytical problems, coding agent behavior and decision-making logic, configuring the agent backend, evaluating agent outputs, and identifying any shortcomings in the domain-specific language or agent architecture for the product development team.

Our ideal candidate is much more than a typical data analyst or AI researcher. We are looking for a customer-facing applied AI and wargaming analyst who can effectively represent military decision problems, behaviors, constraints, objectives, and workflows within a proprietary agent-based simulation architecture. If you're interested in answering this challenge, we'd love to hear from you. Join our mission and innovate with purpose.

What You Must Have:

Bachelor's degree in computer science or a related field; a graduate-level degree is preferred

Experience in artificial intelligence, with a focus on AI agent reinforcement learning -and agent-based simulation

Proficiency in understanding and configuring agent roles, goals, behaviors, interactions, state, decision logic, and emergent behavior within a simulation environment

Capability to read, write, customize, test, and document a specialized language or configuration grammar used to express wargaming logic

Experience with deployment engineering (e.g., build systems and containerization), Docker, Kubernetes, cloud-deployment (e.g., AWS gov cloud), and proficient with logging and monitoring systems to maintain high up time.

Experience working with customers to define analytical questions, decision contexts, assumptions, measures, constraints, and experimental designs

Skills in testing agent behaviors, inspecting traces, debugging unexpected outcomes, documenting uncertainties, and clearly communicating limitations

Ability to configure agent runtime settings, data/context sources, tool access, execution parameters, integration points, and versioned baselines

Proficient in explaining complex agent behaviors and domain-specific language (DSL) logic to non-developer military users, as well as communicating field issues to engineering teams

Capacity to translate customer needs and observed shortfalls into product requirements, bug reports, feature requests, and acceptance criteria

Active or eligible for a security clearance, as required by contract

What Would be Nice to Have:

5+ years of experience in artificial intelligence, with a focus on agent-based simulation

Experience with agent-based modeling, AI-enabled simulations, decision-support tools, wargaming systems, and human-machine teaming

Proficient in domain-specific languages (DSLs), scripting languages, behavior trees, rule engines, planning systems, multi-agent systems, and simulation configuration languages

Skilled in Python, YAML/JSON, SQL, Git, CI/CD pipelines, testing frameworks, logging, tracing, and configuration management

Familiar with large language model (LLM)-enabled agents, symbolic AI, planning algorithms, reinforcement learning, behavior modeling, and cognitive architectures, as they pertain to architecture

Experience in military analysis, campaign analysis, mission analysis, operational planning, or experimentation

Knowledge of AI risk management, model evaluation, traceability, human oversight, and responsible AI documentation