Artificial Intelligence/Machine Learning SME

As an Artificial Intelligence/Machine Learning Subject Matter Expert, you’ll be leading strategic initiatives that establish and grow MTSI’s AI and data engineering capabilities. Your technical vision coupled with hands-on experience will shape reference architectures and charge forward on delivery of secure, scalable solutions. Your role includes translating customer and mission needs into practical solutions, guide technology investments, and moving capabilities from experimentation into sustained production operations, spanning data engineering, analytics, artificial intelligence, machine learning, and MLOps. Day‑to‑day responsibilities include leading AI/ML development and integration of machine learning and generative AI capabilities, including retrieval-augmented generation, model adaptation, evaluation, and inference services for data fabric systems. You’ll be leveraging  ML Operation and LLM Operation technic and workflows for data and model versioning, experiment tracking, automated testing, automated deployment, monitoring, retraining, rollback, and retirement. Address model drift, performance degradation, operational cost and reproducibility.  You’ll have expert knowledge and experience; in data modeling, distributed data processing, relational, graph, vector and time series databases and data lakes, with proven experience delivering and operating production AI/ML capabilities, including the data pipelines and operational processes that support them.  Help define evaluation, human oversight, auditability, and safeguards appropriate to each use case, including protection of sensitive data and management of third-party model dependencies. Work Location: This position is a hybrid position. Work location will be determined by security classification.  Classified work will be performed on-site at MTSI’s Alexandria office. Remote work is not available. Unclassified work can be performed remotely or on-site at MTSI’s Alexandria office. You’ll be a great fit for this role if: ( SOFT SKILLS ) Proactive problem‑solver, able to troubleshoot complex issues across applications, containers, databases, and cluster infrastructure. Strong collaboration skills, with a proven ability to work closely with software, UX, QA, platform, and product teams in fast‑moving mission environments. Effective communication of technical concepts to diverse stakeholders, including coaching mid-level developers and guiding engineering teams. Demonstrate leadership in Agile environments, contributing to sprint planning, backlog refinement, demos, and cross‑team knowledge sharing.  Adaptable in dynamic mission environments, with experience supporting rapid modernization and evolving architectural patterns. Detail‑oriented and committed to secure, maintainable engineering practices through documentation, design reviews, and continuous improvement. Responsibilities: Your essential job functions will include but may not be limited to: AI/ML Engineering: Lead development and integration of machine learning and generative AI capabilities, including retrieval-augmented generation, model adaptation, evaluation, and inference services AI Strategy and Investment: Develop an AI and data capability roadmap aligned with business initiatives, customer needs and mission outcomes. Guide internal research and development, technology selection, partnerships, and build-versus-buy decisions. MLOps and LLMOps: Establish repeatable workflows for data and model versioning, experiment tracking, automated testing, deployment, monitoring, retraining, rollback, and retirement. Address model drift, performance degradation, operational cost and reproducibility. Data Architecture: Design architectures for data ingestion, integration, storage, processing, discovery, and consumption across cloud, on-premises, and hybrid environments. Data Engineering: Lead implementation of reliable batch and streaming pipelines, data products, APIs and reusable integration patterns for structured and unstructured data. Data Governance: Establish practices for data ownership, quality, metadata, lineage, provenance, access control, retention and interoperability working with CDO. Platform Integration: Partner with platform engineering and infrastructure teams to integrate AI and data services with Kubernetes, CI/CD, infrastructure as code, identity, observability, and enterprise security controls. Responsible AI and Security: Define evaluation, human oversight, auditability, and safeguards appropriate to each use case, including protection of sensitive data and management of third-party model dependencies. Mission and Engineering Integration: Connect AI and data capabilities with Digital Engineering, high fidelity MS&A, MBSE, engineering tools and mission analysis workflows Technical leadership: Mentor engineers and data scientists, establish engineering standards, lead architecture reviews and help recruit a multidisciplinary team Qualifications Required: 18+ years combined relevant technical experience in AI, data, software development and platform development. 12+ years of technical leadership experience in a technical field. Demonstrated experience delivering and operating production AI/ML capabilities, including the data pipelines and operational processes. Hands-on proficiency with Python, SQL, APIs and modern software engineering practices including DevSecOps and Agile methods. Experience implementing DataOps and MLOps workflows, including model evaluation, versioning, automated deployment and production monitoring Experience with cloud platforms, containers, Kubernetes, CI/CD and infrastructure automation Ability to translate business and mission objectives into technical architectures, prioritized roadmaps and measurable outcomes Proven ability to lead across software engineering, data science, infrastructure, cybersecurity and business teams Strong written and verbal communication skills, including communication with customers and executive leadership Additional Skills: Experience developing functional systems, system architecture and working prototypes, using Model-based Systems Engineering approach. Exposure and understanding of modeling and simulation practices and data types. Education: Bachelor’s degree in computer science, engineering, mathematics, data science, or a related field, or equivalent relevant experience. Citizenship: Must be a US Citizen. Clearance Requirements : Must have Secret DoD Security Clearance. #LI-MS1 #LI-HYBRID