AI/ML Engineer

Mission Impact 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) Lead the design and implementation of advanced AI/ML features, pipelines, and model‑driven capabilities, including architecting data flows, directing experimentation, and overseeing deployment of models into secure production environments. Architect and optimize event‑driven and microservice‑based systems, developing high‑performance components that leverage platforms like Apache Kafka for mission‑critical real‑time processing. Drive the development of cloud‑native solutions across AWS, Azure, or GCP, guiding containerization strategies, Kubernetes deployment patterns, and infrastructure‑as‑code implementations. Champion DevSecOps best practices by defining CI/CD architectures, implementing automated test frameworks, and maturing infrastructure automation across multiple enclaves. Ensure solutions align with open/reference architectures and interface standards; make architectural recommendations that influence long‑term technical direction across mission systems. Provide senior‑level leadership within Agile teams—leading design reviews, shaping architectural decisions, evaluating technical trade space, and coordinating efforts across multiple stakeholder groups. Produce high‑quality technical documentation and deliver clear, concise briefings to senior stakeholders, mission owners, and engineering leadership. You'll Be a Great Fit If You… 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. Responsibilities (Expanded) Architect, build, and optimize robust batch and streaming data pipelines supporting feature engineering, training workflows, cross‑domain data movement, and operational telemetry. Lead Kubernetes‑based deployments, defining configuration standards, scaling strategies, observability patterns, and resilience mechanisms across multi‑cluster and multi‑enclave environments. Design and tune Kafka ecosystems—topics, schemas, consumer groups—and develop scalable stream‑processing solutions to support mission‑critical analytics. Lead development of automated ML workflows (Airflow, Prefect, etc.) for training, evaluation, versioning, deployment, rollback, and lifecycle governance. Define and mature CI/CD automation frameworks ensuring hardened builds, intelligent test automation, security scanning, artifact governance, and reliable multi‑environment release processes. Develop and maintain architectural and compliance documentation aligned with Government Reference Architectures and mission integration standards. Provide technical oversight, mentor junior and mid‑level engineers, guide code and design reviews, and contribute to continuous improvement of engineering practices, toolchains, and architectural patterns. Minimum Qualifications 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++ . Preferred/Bonus 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. #LI-BG1 #onsite #MTSI