Associate Director, MLOps Engineering

PathAI's mission is to improve patient outcomes with AI-powered pathology. As the Associate Director, MLOps Lead, you will lead the team responsible for the infrastructure that bridges ML research and large-scale production, driving the scalability and efficiency of our Machine Learning Operations platform.

Responsibilities:

• Develop and execute the long term vision & roadmap for MLOPs team to support ML development and deployment needs across the business units
• Successfully manage the tension between short-term tactical deliveries and long-term architectural transformation for future growth
• Lead and mentor a team of 6-7+ high-performing engineers
• Strategically allocate resources to manage support for existing services while executing key strategic initiatives
• Partner with leaders across machine learning, data science, product engineering, and infrastructure to proactively identify pain points, address bottlenecks, and facilitate the deployment of new solutions
• Architect the compute and storage pipelines required for ML Engineers to manage millions of slides and complex derived artifacts without data fragmentation or synchronization latency
• Modernize the AI Product inference stack to support 5-10x growth of AI runs across global deployments
• Collaborate with Site Reliability Engineering (SRE) to establish comprehensive metrics covering compute under-utilization, network bottlenecks, and granular cost and turn-around-time attribution
• Conduct "Build vs. Buy" assessments, leading "Stack Refresh" audits to benchmark our proprietary tools against best-in-class commercial and open-source alternatives to meet our future needs

Requirements:

• Bachelor's or Master's degree in Computer Science, Engineering, or a related field (or equivalent experience)
• 2-3+ years of experience managing engineering team(s), with a focus on building production-grade frameworks for MLOps or ML Infrastructure
• 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)
• Proven 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
• Use of AI assistants (e.g. CoPilot, Cursor, Claude) across platform development lifecycle
• Exposure to 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