Associate Director – MLOps Engineering

PathAI

. Develop and execute the long-term vision and roadmap for the MLOps team .

Posted 9/24/2026full-timeBoston • Massachusetts • United StatesSenior💰 $181,500 - $278,300 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in MLOps and ML Infrastructure, with a strong focus on architecting scalable solutions and managing engineering teams. Proven ability to balance tactical and strategic initiatives while driving platform adoption and optimizing resource allocation.

Highest-signal resume keywords

MLOps Framework DevelopmentKubernetes ExpertiseCloud Computing Platforms (AWS/GCP/Azure)Team Leadership and ManagementWorkflow Orchestration (Airflow, Kubeflow)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Software EngineeringML Workloads ManagementProduction Inference PipelinesInfrastructure-as-Code (Helm, Terraform)Scalable Service Architecture

Soft Skills

MentoringStrategic Resource AllocationProblem Solving

Tools & Technologies

AI Assistants (CoPilot, Cursor, Claude)Observability Metrics Tools

Industry Keywords

MLOpsMachine LearningData ScienceDevOps PrinciplesCloud Costs Management

Tech Stack

Tools & technologies

AirflowAWSAzureCloudGoogle Cloud PlatformKubernetesTerraform

About the role

Key responsibilities & impact

Develop and execute the long-term vision and roadmap for the MLOps team
Balance short-term tactical deliveries with long-term architectural transformation
Lead and mentor a team of 6–7+ engineers
Strategically allocate resources across existing services and strategic initiatives
Partner with machine learning, data science, product engineering, and infrastructure leaders
Identify pain points, address bottlenecks, and facilitate deployment of new solutions
Architect compute and storage pipelines for millions of slides and complex derived artifacts
Modernize the AI product inference stack to support 5–10x growth across global deployments
Collaborate with Site Reliability Engineering to establish observability metrics for compute utilization, network bottlenecks, cost, and turnaround time
Lead Build vs. Buy assessments and Stack Refresh audits

Requirements

What you’ll need

Bachelor’s or Master’s degree in Computer Science, Engineering, or a related field (or equivalent experience)
8–10+ years in Software/ML Engineering
4+ years managing engineering teams and platform strategy
Experience building production-grade frameworks for MLOps or ML Infrastructure
Proven track record of growing engineering teams, managing team budgets/cloud costs, and driving MLOps platform adoption across multi-disciplinary organization units
Deep technical expertise with ML workloads on Kubernetes, cloud computing platforms (AWS/GCP/Azure), workflow orchestration (Airflow, Kubeflow, or proprietary equivalents), and DevOps principles and infrastructure-as-code (Helm, Terraform)
Experience managing petabyte-scale datasets and high-throughput production inference pipelines
Strong software engineering skills in complex, multi-language systems and experience with scalable service architecture
Experience using AI assistants such as CoPilot, Cursor, or Claude across platform development lifecycles
Relocation benefits are not available for this position

Benefits

Comp & perks

Relocation benefits are not available for this position
Equal opportunity workplace free of harassment and discrimination