AI Systems Engineer

For the OPS Consulting team, 'the power to help' means helping our clients, helping serve the mission, helping our employees and their families, and helping the community. Headquartered in Hanover, MD. OPS Consulting has over two decades of experience specializing in the most mission-critical operations. We are thought leaders and innovators. The ingenuity of our developers, engineers, cyber experts, linguists, and analysts is dedicated to empowering our clients, fulfilling The Mission, and remaining trusted leaders and advisers in national security and technology solutions.

We are looking for an AI Systems Engineer to join in Annapolis Junction, MD.

The AI Systems Engineer will apply systems engineering lifecycle processes-requirements analysis, architecture design, integration, test and evaluation, and requirements traceability-to the design, development, and deployment of artificial intelligence and autonomous agent systems within defense and intelligence environments. Operates as a contributing engineer on multi-disciplinary teams developing AI-enabled capabilities, applying established SE principles (ISO/IEC/IEEE 15288, INCOSE SE Handbook, DoD 5000 series) to emerging AI system architectures, including multi-agent orchestration platforms, governance-enforced automation pipelines, and machine-readable specification frameworks.

Responsibilities:

• Perform requirements decomposition for AI-enabled systems, translating mission-level key performance parameters into hierarchical requirement structures (system requirements, sub-requirements, design specifications) with threshold/objective tolerance bounds at each level, traceable to stakeholder needs
• Develop and maintain requirements traceability matrices and dependency maps linking concept of operations, system requirements documents, interface control documents, and test plans to ensure complete bidirectional traceability across AI system artifacts, including configuration management of AI system baselines, model versions, and governance specifications
• Design and implement multi-agent orchestration patterns applying the separation of concerns principle, isolating planning, execution, and governance into independent architectural components where the executing element cannot self-authorize, and compliance is verified independently
• Build and maintain structured input pipelines using retrieval-augmented generation, hybrid search architectures, and context ranking algorithms to optimize AI model performance against mission-specific requirements, including data pipeline assessment, training data quality evaluation, and input provenance verification
• Implement confidence-based escalation logic with defined thresholds for autonomous operation, human-in-the-loop intervention, and mandatory halt conditions, applying the KPP threshold/objective model to agent decision boundaries
• Design and integrate mandatory termination mechanisms (software, hardware, deadman, and scope-boundary) for all autonomous agent deployments, ensuring human override capability at all operational levels
• Support AI TEVV activities, including developmental test planning for non-deterministic systems, adversarial testing, model behavioral boundary verification, prompt injection defense assessment, and readiness scoring using structured green/yellow/red evaluation criteria
• Develop and maintain runtime monitoring components that enforce complete mediation of agent actions, detect behavioral anomalies, and generate cryptographically signed evidence chains with forward integrity
• Perform structured gap analysis and technical debt quantification for AI programs using document cross-referencing, entry/exit criteria validation, and compliance delta assessment against SE standards and program baselines
• Contribute to the centralization of program terminology through controlled vocabulary development, ensuring consistent definition and usage of technical terms across AI system documentation and cross-functional teams
• Support rapid prototyping efforts within gated development cycles, including intake scoring, feasibility assessment, milestone gate reviews, and validation framework execution across technical, user, security, and transition readiness dimensions
• Prepare and deliver technical documentation including concept of operations, system architecture descriptions, interface control documents, and test plans for AI-enabled systems aligned to defense acquisition milestone requirements (SRR, PDR, CDR)
• Assist in the development of AI practitioner training materials covering structured prompting techniques, retrieval-augmented workflows, and multi-agent automation design for defense and intelligence practitioners
• Support ML model lifecycle management including performance monitoring, acceptance criteria validation, retraining trigger identification, and model versioning within governed development environments
• Support operational transition planning for AI-enabled capabilities including user acceptance testing, training delivery, handoff documentation, and sustainment planning from prototype through fielded operation

Requirements:

• Bachelor's degree in Systems Engineering, Computer Science, Computer Engineering, Cybersecurity, Artificial Intelligence, or a closely related technical discipline.
• A Master's degree in a related field may substitute for two (2) years of general experience.
• An additional four (4) years of directly relevant experience may substitute for the Bachelor's degree requirement.
• Minimum of eight (8) years of combined experience in systems engineering, software engineering, cybersecurity, or AI/ML development, with at least three (3) years of specialized experience in one or more of the following: requirements engineering and decomposition for complex systems, autonomous agent system design and governance architecture, multi-agent orchestration and integration, AI-augmented cybersecurity operations, or systems engineering lifecycle application (requirements analysis through test and evaluation) to AI-enabled programs.
• US citizenship and an active TS/SCI with FSP security clearance required

Preferred Qualifications:

• Experience applying SE lifecycle milestones (SRR, PDR, CDR) and associated entry/exit criteria to AI-enabled system development programs
• Familiarity with MBSE tooling (SysML, DOORS, Cameo, or equivalent) and model-based requirements traceability for complex system architectures
• Experience with agent orchestration frameworks (e.g., LangChain, LangGraph, CrewAI, AutoGen, or equivalent) in structured development environments
• Familiarity with containerized deployment (Docker, Kubernetes) in air-gapped or IL4/IL5 environments
• Experience developing or contributing to AI governance policy documentation aligned to NIST AI RMF, ISO/IEC 42001, or DoD Responsible AI principles
• Familiarity with NIST SP 800-171 or CMMC controls as applied to AI development environments handling Controlled Unclassified Information (CUI)
• Understanding of retrieval-augmented generation pipelines, vector databases, and hybrid search architectures
• Experience with structured program assessment methodologies including gap quantification, dependency mapping, and phased review processes (e.g., Blue/Pink/Red/Gold reviews or equivalent staged maturity gates)

Preferred Certifications:

• CISSP (Certified Information Systems Security Professional)
• CISM (Certified Information Security Manager)
• CompTIA SecAI+ (CY0-001) - AI Security
• AWS Certified Machine Learning - Specialty or equivalent cloud AI certification
• CompTIA Security+ (with commitment to obtain CISSP or CISM within 12 months)
• NVIDIA Certified Associate - Generative AI or equivalent vendor AI certification