DevOps​/Platform Engineer II - Richland, WA

Overview

At PNNL, our core capabilities are divided among major departments that are referred to as directorates within the Lab, focused on a specific area of scientific research or other function, with its own leadership team and dedicated budget. Our Science & Technology directorates include National Security, Earth and Biological Sciences, Physical and Computational Sciences, and Energy and Environment. In addition, we have an Environmental Molecular Sciences Laboratory, a Department of Energy Office of Science user facility housed on the PNNL campus.

The National Security Directorate (NSD) drives science-based, mission-focused solutions to take on complex, real‑world threats to our nation and the world. The AI and Data Analytics Division, part of NSD, combines profound domain expertise and creative integration of advanced hardware and software to deliver computational solutions that address complex data and analytic challenges. Working in multidisciplinary teams, we connect foundational research to engineering to operations, providing the tools to innovate quickly and field results faster.

Our strengths are integrated across the data analytics lifecycle, from data acquisition and management to analysis and decision support.

We are seeking a Dev Ops/Platform Engineer to join PNNL's AI engineering team, contributing to innovative systems spanning agentic AI platforms, large-scale data orchestration, and real-time intelligence processing. This is an excellent opportunity for early to mid-career developers to apply their software engineering skills to meaningful national security challenges while growing their expertise in AI/ML systems, cloud infrastructure, and distributed computing.

You're a motivated software engineer with foundational experience in building production systems and a strong desire to grow your expertise in AI/ML and scalable infrastructure. You're comfortable working both independently on defined tasks and collaboratively on larger initiatives. You're eager to learn new technologies, apply software engineering best practices, and contribute to mission-critical systems while building your professional network and technical reputation.

• Develop components of agentic AI systems and LLM-based applications
• Implement features using frameworks like Lang Chain, Llama Index, or similar tools
• Build and maintain ML pipelines, data preprocessing workflows, and model deployment infrastructure
• Create utilities and tools that support AI/ML development and operations
• Work with multi-modal data including text, structured data, and sensor information

• Build data pipelines for large-scale ETL, transformation, and analytics workflows
• Implement streaming data processors and event-driven components
• Develop microservices and APIs within distributed architectures handling high-throughput workloads
• Deploy containerized applications using Docker and Kubernetes
• Contribute to CI/CD pipelines and automated testing frameworks

• Write clean, well‑tested code following established best practices
• Implement monitoring, logging, and observability for applications
• Build developer tooling and documentation to support team productivity
• Contribute to system performance optimization and debugging efforts
• Support deployments in cloud and secure environments

• Work on small tasks and project elements, progressing to independent ownership
• Collaborate with cross‑functional teams including data scientists, researchers, and senior engineers
• Participate in code reviews, design discussions, and technical planning
• Mentor junior staff and students when opportunities arise
• Contribute technical content to proposals and project documentation
• Present your work at team meetings and technical forums

• Working proficiency in Python with foundational knowledge of at least one additional language (Bash, Go, C#, JavaScript/Type Script) for scripting and automation tasks
• Understanding of Infrastructure as Code principles with…