Associate Director, MLOps & Platform Engineering

PathAI; Boston (MA); Hybride; USD 182 000 - 278 000; Plein temps

Résumé du poste

PathAI, based in Boston, MA, is seeking an Associate Director, MLOps Lead to scale our ML infrastructure and production pipelines. You will own the roadmap for the MLOps stack, supervise a team of engineers, and partner with ML, data science, and infrastructure groups to deliver reliable, scalable solutions.

You will drive advances in high-scale training and inference workloads, data pipelines, and observability, ensuring efficient operation across global deployments and alignment with security

Qualifications

• Bachelor's or Master's in Computer Science, Engineering, or related field (or equivalent experience).
• 8–10+ years in Software/ML Engineering, with 4+ years managing engineering teams and platform strategy.
• Proven track record growing engineering teams, managing budgets/cloud costs, and driving MLOps platform adoption.

Responsabilités

• Lead and mentor a 6-7+ engineer team to scale ML infrastructure and services.
• Develop and execute the long-term vision and roadmap for MLOps supporting ML development and deployment.
• Collaborate with ML, data science, product engineering, and infrastructure to address bottlenecks and deploy new solutions.
• Architect compute and storage pipelines for large-scale data and artifacts without fragmentation or latency.

Connaissances

MLOps leadership

Kubernetes

Cloud platforms

CI/CD for ML

Data engineering

Observability

Team management

Formation

Bachelor's or Master's in CS/Engineering

Outils

Airflow

Kubeflow

Terraform

Helm

Databricks

Spark

Description du poste

PathAI, based in Boston, MA, is seeking an Associate Director, MLOps Lead to scale our ML infrastructure and production pipelines. You will own the roadmap for the MLOps stack, supervise a team of engineers, and partner with ML, data science, and infrastructure groups to deliver reliable, scalable solutions.

You will drive advances in high-scale training and inference workloads, data pipelines, and observability, ensuring efficient operation across global deployments and alignment with security