Machine Learning & Operations Engineer

Role Overview

OptiTrack is seeking a Machine Learning & Operations Engineer to design, automate, and scale MLOps systems. You’ll bridge machine learning engineering and infrastructure, focusing on data validation pipelines, large-scale experimentation, and deploying high-performance algorithms.

Responsibilities

• Design, implement, and maintain automated ML training pipelines
• Build and optimize infrastructure for large-scale distributed experimentation
• Develop CI/CD workflows tailored to machine learning systems
• Orchestrate data ingestion, preprocessing, validation, and model versioning
• Implement experiment tracking, hyperparameter tuning automation, and reproducibility systems
• Optimize GPU and compute utilization across cloud and on-prem environments
• Deploy, monitor, and maintain production-grade ML models
• Establish MLOps best practices (e.g., model registry, artifact management, observability)
• Improve reliability, security, and performance through continuous improvements
• Collaborate with ML researchers to make new algorithms production-ready
• Perform typical DevOps tasks as needed

Requirements

• 3+ years experience in MLOps, ML infrastructure, or related roles (or relevant degree)
• Proficiency in Python and ML frameworks such as PyTorch or TensorFlow
• Experience building CI/CD pipelines (e.g., GitHub Actions, GitLab CI, Jenkins)
• Hands-on experience with Docker and orchestration tools
• Experience managing GPU workloads and distributed training systems
• Familiarity with cloud platforms such as AWS, GCP, or Azure
• Strong understanding of automation, infrastructure reliability, and data pipelines
• Ability to collaborate effectively with international teams

Nice-to-haves

• None explicitly stated beyond the listed requirements.