Senior MLOps Leader — Scale & Reliability

PathAI; Boston (MA); Hybrid; USD 182,000 - 278,000; Full time

Job summary

PathAI in Boston is seeking an Associate Director, MLOps Lead to guide the team building our ML infrastructure and production pipelines. You will oversee high-scale AI training and inference workloads, cloud infra, Kubernetes, observability, and IaC practices across global deployments.

The role emphasizes design for reliability, collaboration, and continuous improvement, with a hybrid work model in Boston and a strong focus on evolving the ML stack to meet scale and performance goals.

Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or related field (or equivalent).
• 8–10+ years in Software/ML Engineering with 4+ years managing teams and platform strategy.
• Experience building production-grade MLOps or ML infrastructure frameworks.
• Proven track record growing engineering teams, managing budgets and driving MLOps adoption.

Responsibilities

• Vision and roadmap for the MLOps team to support ML development and deployment needs.
• Lead and mentor a team of 6-7+ engineers and allocate resources for ongoing services and strategic initiatives.
• Collaborate with ML, data science, product, engineering, and infrastructure to deploy new solutions.
• Architect compute and storage pipelines for millions of slides and artifacts without fragmentation.
• Modernize the AI product inference stack for 5-10x growth across global deployments.
• Work with SRE to establish metrics for utilization, bottlenecks, cost and turnaround time.
• Conduct Build vs. Buy assessments and Stack Refresh audits for future needs.

Skills

Kubernetes

Cloud platforms

Workflow orchestration

DevOps

Infrastructure as code

Petabyte-scale data

Production inference

ML workloads

Education

Bachelor's or Master's in CS/Engineering

Tools

Airflow

Kubeflow

Terraform

Helm

PyTorch

Scikit-learn

Databricks

Job description

PathAI in Boston is seeking an Associate Director, MLOps Lead to guide the team building our ML infrastructure and production pipelines. You will oversee high-scale AI training and inference workloads, cloud infra, Kubernetes, observability, and IaC practices across global deployments.

The role emphasizes design for reliability, collaboration, and continuous improvement, with a hybrid work model in Boston and a strong focus on evolving the ML stack to meet scale and performance goals.