Lead DevOps​/Platform Engineer IV - Richland, WA

Overview

At PNNL, our core capabilities are divided among major directorates that focus on specific areas of scientific research or other functions, with their 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 drives science‑based, mission‑focused solutions to complex, real‑world threats to our nation and the world. The AI and Data Analytics Division, part of NSD, combines 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 and 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 Lead Dev Ops/Platform Engineer to join PNNL’s advanced AI engineering initiatives, contributing to next‑generation systems spanning agentic AI platforms, large‑scale data orchestration, and real‑time intelligence processing. In this role, you’ll apply your expertise in scalable system design and AI/ML engineering to build mission‑critical capabilities while developing your technical leadership and establishing yourself as a key contributor to our engineering community.

You're an accomplished engineer with strong foundations in Dev Ops, scalable system design, AI/ML development, and production software engineering. You're ready to take on increasing technical responsibility, leading components of complex systems while mentoring junior team members. You excel at translating technical requirements into working solutions, selecting appropriate approaches for challenging problems, and contributing meaningfully to technical direction and project success.

• Design and deploy scalable agentic AI systems with dynamic reasoning and decision‑making capabilities
• Architect LLM orchestration frameworks using Lang Chain, Llama Index, and emerging agent platforms
• Build MLOps platforms spanning experiment tracking, model versioning, deployment, and governance
• Develop developer‑focused tooling, adapters, and interfaces for AI‑native frameworks
• Integrate multi‑modal data sources (text, vision, structured/sensor data) into cohesive reasoning pipelines

• Design microservices architectures coordinating across multiple domains and security enclaves
• Lead distributed system design processing data from hundreds of sources simultaneously
• Architect real‑time streaming platforms handling terabytes per hour with event‑driven architectures
• Build robust data pipelines for petabyte‑scale ETL, data lake/mesh architectures, and real‑time analytics
• Design container orchestration (Kubernetes) and CI/CD pipelines for classified and edge environments

• Deploy AI systems in highly secure environments with resilient agent‑to‑agent communications
• Create monitoring and observability systems (logging, metrics, tracing) across secure enclaves
• Ensure compliance with ethical AI standards and security‑first Dev Ops practices
• Build geospatial processing, time‑series, and intelligence data fusion capabilities

• Lead a team of engineers to deliver on high‑risk, high‑impact ambiguous technical scope
• Drive technical strategy and architectural decisions across cross‑functional teams
• Translate ambiguous requirements and cutting‑edge research into actionable technical roadmaps
• Lead design discussions shaping team‑wide engineering standards
• Mentor engineering teams and guide junior scientists/engineers

• Expert‑level proficiency in Python and at…