Senior Machine Learning Engineer

Role Overview

RAZOR is seeking a Senior MLOps Engineer to own infrastructure for AI/ML systems, including pipelines, deployments, and GPU environments.

Responsibilities

• Build GPU infrastructure for AI/ML workloads
• Develop and improve CI/CD pipelines
• Automate deployments
• Optimize inference performance
• Implement monitoring for ML/infrastructure operations
• Manage infrastructure as code (IaC)
• Collaborate with ML teams to support the end-to-end ML lifecycle

Required Qualifications

• 5+ years of MLOps/DevOps experience
• AWS expertise
• Terraform experience
• Knowledge of Docker and GPU environments
• Python and/or Bash scripting
• Understanding of the ML lifecycle

Preferred Qualifications

• Experience with Generative AI
• Experience with air-gapped environments
• Familiarity with MLflow or Weights & Biases (W&B)
• Federal experience

Location & Work Setup

• Remote (U.S.) or McLean, VA (on-site or hybrid)
• Occasional travel to McLean, VA for team onsites/planning and infrastructure access
• Monday–Friday with occasional off-hours availability for infrastructure incidents, deployment windows, or training job monitoring

Success Milestones

• 30 days: Understand existing systems
• 90 days: Improve pipelines
• 6 months: Scale infrastructure