Associate Director, MLOps Engineering

Sourced from feeny.ai

PathAI is seeking an Associate Director, MLOps Lead to lead the team responsible for the backbone of their AI/ML stack. This role oversees infrastructure bridging ML research and massive-scale production, evolving the stack for large-scale ML training and inference workloads. The position involves team management, cross-functional collaboration, and driving MLOps platform adoption across business units.

• Develop and execute long-term vision and roadmap for the MLOps team
• Lead and mentor a team of 6-7+ high-performing engineers
• Partner with leaders across ML, data science, product engineering, and infrastructure
• Architect compute and storage pipelines for millions of slides and complex artifacts
• Modernize AI product inference stack to support 5-10x growth
• Collaborate with SRE to establish comprehensive system observability metrics
• Conduct build vs. buy assessments and lead stack refresh audits

• Bachelor’s or Master’s degree 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
• Experience building production-grade frameworks for MLOps or ML Infrastructure
• Proven track record of growing engineering teams, managing budgets/cloud costs, and driving MLOps platform adoption
• Deep technical expertise with ML workloads on Kubernetes, cloud platforms (AWS/GCP/Azure), workflow orchestration (Airflow, Kubeflow), 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 scalable service architecture
• Experience using AI assistants (e.g., CoPilot, Cursor, Claude) across platform development lifecycles

• Experience with ML frameworks like PyTorch or Scikit-learn
• Experience with large-scale data processing frameworks (e.g., Spark, Hive, Databricks, Amazon EMR)
• Expertise in MLOps principles including model lifecycle management, feature stores, model monitoring, and CI/CD for ML
• Familiarity with security and compliance best practices in ML systems