AI/ML Engineer

At MTSI, you’ll architect and deliver AI/ML‑enabled, cloud‑native mission software that operates across platforms, weapons, and terrestrial systems. Your work will modernize enterprise and event‑driven architectures, enabling rapid, secure capability delivery to the warfighter in highly contested environments.

What You’ll Do (Day‑to‑Day)

• Design and implement AI/ML features and pipelines, including data preparation, experimentation, model training, and deploying models into production environments.
• Build and enhance event‑driven and microservice‑based components that integrate with platforms such as Apache Kafka for real‑time data processing.
• Develop and maintain cloud‑native applications on AWS, Azure, or GCP using containerization, Kubernetes, and infrastructure‑as‑code patterns.
• Contribute to DevSecOps practices by building CI/CD pipelines, implementing automated tests, and supporting infrastructure automation.
• Apply open/reference architectures and interface standards to ensure interoperability and technical alignment across mission systems.
• Collaborate within Agile teams (Scrum/Kanban), contributing to planning, design reviews, technical assessments, and cross‑team coordination.
• Produce clear technical documentation and contribute to briefings for stakeholders and senior engineering staff.

• Are eager to grow your AI/ML engineering skills and enjoy turning algorithms or prototypes into reliable, maintainable code.
• Are curious about event‑driven architectures, resilient systems, and real‑time data streaming.
• Thrive in collaborative, fast‑paced Agile environments and enjoy learning from peers and senior engineers.
• Are comfortable working across the stack—from data pipelines to model deployment to cloud infrastructure.

• Design, build, and maintain data pipelines (batch and streaming) that support feature engineering, model training, and operational telemetry.
• Deploy and manage workloads on Kubernetes, including configuration, scaling strategies, observability, and troubleshooting.
• Configure and optimize Kafka topics, schemas, and consumer groups; contribute to stream‑processing solutions and performance tuning.
• Build and manage automated ML workflows (Airflow, Prefect, etc.) for model training, evaluation, versioning, deployment, and rollback.
• Develop and maintain CI/CD pipelines, ensuring automated builds, tests, security scanning, and artifact management.
• Contribute to compliance documentation for Government Reference Architectures and integration standards.
• Participate in code reviews, architecture discussions, and continuous improvement activities; contribute solutions and mentor junior engineers as appropriate.

• Bachelor’s degree in Computer Science, Computer Engineering, Systems Engineering, or related field.
• Professional software experience
• Experience building cloud‑native solutions on AWS/Azure/GCP; understanding of IaaS/PaaS, networking, security, and cost management.
• Hands‑on Kubernetes experience: container orchestration, Helm, ingress, service mesh, scaling, and troubleshooting.
• Practical AI/ML delivery experience: model lifecycle (data prep, training, validation, deployment, monitoring) and MLOps practices.
• Proven Agile experience (Scrum/Kanban) and toolchains (e.g., Jira/Confluence) for planning, tracking, and documentation.
• Strong software engineering fundamentals (design patterns, testing, code reviews) and proficiency with at least one of: Python, Java, C++.

• Kubernetes certification (CKA, CKAD, or CKS).
• Experience with stream processing frameworks (Kafka Streams, Flink, Spark Streaming).
• MLOps platforms (SageMaker, Vertex AI, MLflow) and feature stores.
• Infrastructure as Code (Terraform), container security, and SBOM/zero‑trust practices.

• United States citizenship is required with the ability to obtain a secret security clearance

#onsite