Senior MLOps Engineer: Production ML Pipelines & Registry

Zeitview; Boston (MA); On-site; USD 170,000 - 180,000; Full time

Benefits offered by this job

Base salary $170,000 – $180,000 USD

Job summary

Responsibilities include maintaining the model registry, building deployment pipelines, and implementing monitoring and governance for ML systems. The role blends R&D, software, and DevOps in a fast-paced environment.

Qualifications

• Bachelor's degree in CS, software engineering, data engineering, or related field.
• 4+ years of professional experience in MLOps, ML platform engineering, or infrastructure engineering supporting machine learning teams.
• Solid, applied knowledge of MLOps practices and ability to work independently across varied production scenarios.
• Strong Python skills and solid software engineering fundamentals (testing, code review, version control).
• Hands-on experience with AWS, Terraform, Github Actions, Docker, and Kubernetes.
• Experience building production ML pipelines and model registries, including versioning and safe releases.
• Familiarity with computer vision or geospatial ML pipelines.
• Nice to have: experience operating LLM/Agentic systems in production and data pipelines with PostgreSQL/Hasura.

Responsibilities

• Partner with ML Scientists, Data Scientists, and Perception Engineers to translate research code into dependable production services.
• Coordinate with DevOps and Software Engineering teams on infrastructure requests and data pipelines.
• Maintain and improve model registry and deployment pipelines with safer release practices.
• Build and troubleshoot cloud infrastructure and CI/CD pipelines for ML workloads.
• Implement monitoring and observability for models and pipelines in production and track drift.
• Provide ongoing maintenance and platform support including dependencies and retraining as needed.
• Define and document conventions for model versioning, deployment promotion, and model documentation/lineage.

Skills

Python programming

MLOps knowledge

Software engineering fundamentals

Independent problem solving

Education

Bachelor's degree in Computer Science, Software Engineering, Data Engineering, or related field

Tools

Terraform

Github Actions

Docker

Kubernetes

AWS

Hasura/GraphQL data layers

Job description

Responsibilities include maintaining the model registry, building deployment pipelines, and implementing monitoring and governance for ML systems. The role blends R&D, software, and DevOps in a fast-paced environment.