Junior 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)

• Support the design and development of AI/ML solutions, including preparing training data, running experiments, and helping deploy models into production workflows.
• Contribute to event‑driven and microservice‑based systems by building and testing small components that integrate with platforms such as Apache Kafka.
• Assist in building and maintaining cloud‑native applications on AWS, Azure, or GCP using containerization and Kubernetes.
• Participate in DevSecOps processes by helping configure CI/CD pipelines, set up automated tests, and support infrastructure automation.
• Work within open/reference architectures and follow interface standards to ensure interoperability across mission systems.
• Collaborate on Agile teams (Scrum/Kanban); attend standups, planning sessions, design reviews, and technical discussions with Government and industry partners.
• Draft technical notes, contribute to documentation, and support briefings to senior engineers and stakeholders.

• 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.

• Develop and maintain data pipelines (batch or streaming) that support model training, feature extraction, and system telemetry.
• Assist in managing Kubernetes‑based environments (deployments, health checks, basic scaling strategies).
• Help configure and monitor Kafka topics, schemas, and consumer groups under guidance from senior engineers.
• Support automated workflows (Airflow, Prefect, etc.) for model training, evaluation, and deployment.
• Contribute to CI/CD pipelines through build/test setup, security scanning, and artifact management tasks.
• Help prepare documentation to demonstrate compliance with Government Reference Architectures and technical interface standards.
• Participate in team learning, code reviews, and continuous improvement activities; proactively seek mentorship and share knowledge as you grow.

• 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

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