AIOps Team Lead: Data Scientist for HPC Systems

Los Alamos National Laboratory; Los Alamos (NM); Hybrid; USD 140,000 - 170,000; Full time

Job summary

Los Alamos National Laboratory seeks an experienced AIOps Team Leader to guide data-driven optimization of HPC systems. You will design, build, and run LLMs, ML models, and agent architectures to monitor, analyze, and automate operations across facilities, clusters, and software stacks.

Lead multidisciplinary teams, drive a roadmap for AI/ML in reliability, efficiency, and user support while maintaining strong emphasis on security and responsible AI.

Qualifications

• Bachelor's degree in Computer Science or Computer Engineering or related field with 6 years of experience in HPC or scalable AI computing or data center environments.
• Experience leading multidisciplinary teams of HPC engineers, data scientists, ML engineers, and developers.
• Strong programming skills with Python and AI frameworks for data analysis, model development, automation, APIs, testing, and production-quality services.

Responsibilities

• Monitor and analyze system performance to identify anomalies and patterns.
• Analyze large volumes of data to identify trends using ML/DS techniques and develop automation scripts and workflows.
• Maintain AIOps platforms and tools including user-facing LLMs and assist in documenting findings.

Skills

HPC systems

Linux administration

Technical leadership

Python

ML & data analysis

Monitoring & observability

Root cause & performance analysis

Data engineering

MCP & systems integration

LLM, RAG & AI agent architecture

Security & responsible AI

Education

Bachelor's degree in Computer Science or Computer Engineering

Tools

Splunk

Git

Job description

Los Alamos National Laboratory seeks an experienced AIOps Team Leader to guide data-driven optimization of HPC systems. You will design, build, and run LLMs, ML models, and agent architectures to monitor, analyze, and automate operations across facilities, clusters, and software stacks.

Lead multidisciplinary teams, drive a roadmap for AI/ML in reliability, efficiency, and user support while maintaining strong emphasis on security and responsible AI.