Senior Machine Learning Ops Engineer

Jobtailor; San Francisco (CA); On-site; USD 140,000 - 210,000; Full time

Job summary

Jobtailor seeks an experienced ML Infrastructure Engineer to architect and operate scalable cloud-based MLOps platforms for end-to-end model lifecycle management.

You will own technical strategy, drive cross‑functional initiatives, and mentor engineers while ensuring reliability, observability, and cost efficiency across deployments.

Qualifications

• 6+ years of software engineering with Python and Linux.
• Experience in infrastructure, cloud and/or MLOps.
• Strong collaboration and communication skills.
• Bachelor's or Master's in CS/EE or related field.

Responsibilities

• Architect and deploy scalable cloud-based MLOps platforms and workflows for AI/ML models.
• Own the technical strategy and evolution of ML infrastructure across teams.
• Build automated systems to ship models rapidly with reliability and observability.
• Define infra optimization strategies balancing performance and cost.
• Evaluate new tools and lead their adoption to improve workflows.
• Establish engineering best practices for ML infrastructure and CI/CD.
• Provide mentorship, lead code reviews, and raise team capabilities.
• Partner with ML engineers, researchers, and product teams to translate requirements into scalable solutions.
• Drive complex infrastructure projects from strategy to production ownership.

Skills

Python

Linux

MLOps

Cloud infrastructure

Software engineering

Education

Bachelor's or Master's in CS/EE

Tools

AWS

GCP

CI/CD

Job description

Responsibilities

• Architect, design, deploy, and operate scalable cloud-based MLOps platforms and workflows that enable efficient training, evaluation, deployment, monitoring, and lifecycle management of AI/ML models.
• Own the technical strategy and evolution of ML infrastructure, identifying architectural bottlenecks and driving cross‑functional initiatives to improve developer productivity, experimentation velocity, scalability, and operational efficiency.
• Build robust, reliable, and automated systems that enable teams to ship new models and features rapidly while maintaining high standards for quality, reproducibility, observability, security, and production reliability.
• Define and implement infrastructure optimization strategies that balance performance, scalability, reliability, and cost across cloud and compute resources.
• Evaluate emerging tools, technologies, and industry best practices in MLOps, cloud infrastructure, and ML systems, and lead their adoption where they can meaningfully improve ML development and production workflows.
• Establish engineering best practices for ML infrastructure, including system design, code quality, testing, CI/CD, monitoring, documentation, and operational readiness.
• Provide technical leadership and mentorship to engineers, lead design and code reviews, and help raise the engineering quality and technical capabilities of the broader team.
• Partner closely with ML engineers, researchers, data engineers, and product teams to translate evolving AI/ML requirements into scalable and maintainable infrastructure solutions.
• Drive complex, ambiguous infrastructure projects from technical strategy and architecture through implementation, production deployment, and long‑term operational ownership.

Requirements

• A Bachelors Degree or a Masters Degree in Computer Science, Electrical Engineering, or a related field.
• Core Skills: General Software Engineering skills with 6+ years of programming experience in Python and the surrounding tooling ecosystem along with familiarity in Linux and expertise in infrastructure, cloud and/or MLOps.
• Personal Attributes: Team player, good communication skills, self‑starter.
• Strong teamwork and communication skills to collaborate with cross‑functional teams, including ML and software engineers.
• Nice to Have: Experience building MLOps pipelines for deep learning based perception solutions on AWS or GCP.

Core Competencies

Demonstrates expertise in architecting and deploying scalable cloud-based MLOps platforms, with a strong focus on optimizing ML infrastructure for performance, reliability, and cost. Proven ability to lead cross‑functional teams and mentor engineers while implementing best practices in software engineering and ML workflows.