AI Infrastructure Engineer

Description

We are seeking an AI Infrastructure Engineer to join our Decision Advantage Solutions team within our National Security Sector. In this role, you will build robust, scalable infrastructure to support advanced machine learning and generative AI applications in support of our national defense programs.

Position Overview

This unique role combines responsibilities across MLOps and Data Engineering to support end-to-end AI/ML workflows. As an AI Infrastructure Engineer, you'll be integral in designing and maintaining scalable infrastructure, deploying production-ready models, managing data pipelines, and ensuring the performance and reliability of our AI solutions.

You will work closely with Senior AI/ML Engineers and Senior Data Scientists to ensure that experimentation, model training, and deployment processes are seamless, secure, and mission-ready.

Primary Responsibilities:

• Design and automate end-to-end MLOps pipelines, including CI/CD for model training and deployment.
• Build and maintain scalable ETL/ELT data pipelines to support AI/ML workflows with high-volume, high-velocity datasets.
• Manage production-grade model deployments, including versioning, monitoring, retraining, and rollback strategies.
• Ensure scalable, secure data infrastructure to handle structured and unstructured data at scale.
• Collaborate with cross-functional AI teams to define and meet infrastructure and experimentation requirements.
• Support continuous optimization of the ML lifecycle using modern observability and monitoring tools.

Required Qualifications:

• Bachelor's degree in Computer Science, Engineering, or a related field and 4+ years of experience in MLOps, data engineering, or AI infrastructure.
• Strong expertise in cloud platforms such as AWS, GCP, or Azure.
• Proficiency with infrastructure-as-code tools like Docker, Kubernetes, and Terraform.
• Advanced experience with big data technologies, such as Apache Spark, Hadoop, and Kafka.
• Familiarity with ML observability and orchestration tools, including MLflow, Kubeflow, and Airflow.
• Solid understanding of version control systems, CI/CD, and DevOps best practices.
• Strong collaboration, communication, and documentation skills.
• US citizenship is required and able to obtain and maintain security clearance.

Preferred Qualifications:

• Active Secret or Top Secret clearance.
• Experience working in defense, intelligence, or other national security domains.
• Knowledge of secure system design and data protection best practices in sensitive environments.
• Experience supporting generative AI or large-scale ML model deployments.
• Familiarity with hybrid cloud/on-premise architectures and air-gapped environments.

At Leidos, we don't want someone who "fits the mold"-we want someone who melts it down and builds something better. This is a role for the restless, the over-caffeinated, the ones who ask, "what's next?" before the dust settles on "what's now."

If you're already scheming step 20 while everyone else is still debating step 2... good. You'll fit right in.

For U.S. Positions: While subject to change based on business needs, Leidos reasonably anticipates that this job requisition will remain open for at least 3 days with an anticipated close date of no earlier than 3 days after the original posting date as listed above.

The Leidos pay range for this job level is a general guideline onlyand not a guarantee of compensation or salary. Additional factors considered in extending an offer include (but are not limited to) responsibilities of the job, education, experience, knowledge, skills, and abilities, as well as internal equity, alignment with market data, applicable bargaining agreement (if any), or other law.

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