Senior MLOps / ML Infrastructure Engineer

Role Overview

Senior MLOps / ML Infrastructure Engineer (Contract: 6 months on W2, Remote) joining the core platform team. The role builds scalable, standardized ML workflows and infrastructure to accelerate experimentation and deployment across the organization.

Responsibilities

• Design, develop, and maintain scalable ML workflows and pipelines
• Build and improve ML infrastructure for training and serving, including GKE-based systems
• Automate end-to-end ML workflows to improve efficiency and reduce operational overhead
• Develop robust data sampling and feature generation platforms
• Standardize ML training, deployment, and knowledge distillation pipelines
• Partner with researchers and engineers to enable large-scale ML experimentation
• Drive foundational ML platform tooling and adoption across the org

Key Initiatives

• Scalable ML workflows/pipelines for large-scale ML systems
• Automation of end-to-end ML workflows
• GKE-based training and serving infrastructure
• Knowledge distillation and foundational training tooling

Qualifications

Must-have

• 5–10+ years experience in large-scale ML systems, MLOps, or ML infrastructure
• Strong expertise in ML workflows, distributed systems, and pipeline automation
• Experience with GKE and scalable ML training/serving platforms
• Ownership-driven, collaborative, pragmatic approach; strong communication

Preferred

• Experience at large-scale ML/AI companies (e.g., Google, Meta, Amazon, Microsoft)
• Hands-on MLE/ML infra experience (not purely theoretical or pure DevOps)

Success Metrics / KPIs

• Faster time-to-market for ML experiments
• Improved training efficiency and infrastructure uptime
• Pipeline reliability, cost optimization, and stable deployments
• High platform adoption and reduced onboarding time for ML workflows