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'll need:** - 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 **We prefer to see:** - 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 **Salary Range** $140,000 - $225,000 The above salary range is an estimate based on the internal job level(s) for which this role is being considered. The final salary will be decided after careful evaluation of the individual's work experience, education, and overall qualifications. This range does not include the substantial total rewards, as listed below, that you will also be eligible for as an employee at Charles River Analytics.