Machine Learning Operations (MLOps) Engineer

Role: Machine Learning Operations (MLOps) Engineer

Build and operate the infrastructure behind a modern machine learning platform that powers production-grade AI systems. You’ll design scalable end-to-end workflows for training, deployment, and monitoring, working across software engineering, DevOps, and ML.

Responsibilities

• Design, build, and maintain infrastructure and tooling across the full ML lifecycle (training → experimentation → deployment → monitoring)
• Build scalable ML infrastructure for training, evaluation, deployment, and inference
• Develop and maintain containerized systems using Docker and Kubernetes for distributed workloads
• Build and orchestrate distributed training pipelines and workflow automation
• Use and maintain ML lifecycle tooling such as MLflow (experiment tracking, versioning, reproducibility)
• Own and optimize production inference systems (low-latency, high-availability model serving)
• Implement and maintain CI/CD pipelines for ML models (automated deployment, versioning, rollback)
• Build and manage data pipelines integrated with Snowflake and related data systems
• Implement observability (monitoring, logging, alerting) for model performance, drift detection, and system health
• Partner with ML engineers to improve platform usability, reliability, and developer experience

Requirements

• 5+ years of experience in software engineering, DevOps, or MLOps
• Strong software engineering background with hands-on ML infrastructure operation at scale
• Python proficiency and experience building production-grade distributed systems
• Hands-on experience with Docker and Kubernetes
• Proven experience designing and maintaining CI/CD for production systems
• Familiarity with MLflow (or equivalent)
• Experience with data platforms such as Snowflake (or similar cloud data warehouses)
• Strong system design fundamentals (microservices, APIs, scalable architectures)
• Excellent debugging/troubleshooting in complex distributed environments
• Strong collaboration skills with ML engineers and data teams

Education

• Bachelor’s or Master’s degree in Computer Science/Engineering (or equivalent practical experience)

Benefits (Highlights)

• Fully remote work; unlimited vacation policy
• Comprehensive healthcare coverage (medical, dental, vision)
• 401(k) plan, paid parental leave
• Startup environment focused on engineering impact and automation