2027 Graduate - Junior AI Platform Engineer

Johns Hopkins Applied Physics Laboratory•Laurel, MD

About The Position

The Junior AI Platform Engineer is an early-career role on the platform engineering team that builds and operates enterprise AI platforms and services supporting advanced research at APL. The engineer will use cloud-native technologies, including Docker, Kubernetes, and Helm, to deploy, monitor, and improve scalable infrastructure. Senior engineers provide technical direction, mentorship, and practical guidance as the employee learns modern AI, DevOps, and MLOps practices and progressively takes greater ownership of platform responsibilities.

Requirements

Bachelor's or Master's degree in Computer Science or a related field.
Familiarity with Linux environments and basic system administration concepts.
Familiarity with container technologies such as Docker and orchestration technologies such as Kubernetes.
Familiarity with CI/CD and other DevOps concepts.
Experience with Python or a similar programming language.
Understanding of AI/ML concepts and interest in foundation models and Large Language Models (LLMs).
Familiarity with LLM application patterns, including RAG, tool/function-calling, MCP, and agentic workflows.
Familiarity with agentic harnesses such as Codex, Claude Code, or OpenCode.
Ability to investigate and troubleshoot technical problems using documentation, research, logs, and guidance from more experienced engineers.
Comfort learning and adopting new technologies.
Ability to obtain an Interim Secret security clearance by the start date and ultimately obtain a Secret clearance; eligibility requires U.S. citizenship and applicants selected will be subject to a government security clearance investigation.

Nice To Haves

Demonstrated experience managing applications deployed to Kubernetes, especially using Helm.
Demonstrated experience developing and maintaining CI/CD pipelines and other DevOps artifacts.
Experience with at least one cloud computing platform such as AWS, Azure, or GCP.
Experience securing cloud-native applications.
Proficiency with LLM application patterns.
Proficiency with agentic harnesses.
Hands-on experience with MLOps and Agile processes.

Responsibilities

Deploy, operate, and administer enterprise AI platforms and services using technologies such as Docker, Kubernetes, and Helm.
Develop, enhance, and maintain software and infrastructure that improves platform operations, monitoring, and reliability.
Troubleshoot infrastructure, application, and user issues.
Analyze AI platform reliability and performance by conducting benchmarks, identifying bottlenecks, and implementing improvements.
Participate in design discussions, code reviews, documentation, testing, and other team engineering activities.
Assist in identifying and evaluating emerging AI technologies and assessing their suitability for laboratory use.
Learn and apply platform engineering, DevOps, MLOps, and administration automation practices.
Share knowledge and contribute to a collaborative environment focused on continuous learning and improvement.
Engage in mentorship and technical guidance from senior team members while progressively taking ownership of platform engineering responsibilities.