Associate Director, MLOps Engineering

About the Role

We are seeking an Associate Director, MLOps Lead to join our Machine Learning team. In this position, you will lead the team responsible for the backbone of our AI/ML stack, overseeing the infrastructure bridging ML research and massive-scale production. Your primary directive is to evolve our stack to meet large-scale ML training and inference workload needs.

Key Responsibilities

• Vision and Roadmap: Develop and execute the long-term vision and roadmap for the MLOps team to support ML development and deployment across business units.
• Team Management: Lead and mentor a team of 6–7+ high-performing engineers. Strategically allocate resources to manage service support while executing strategic initiatives.
• Cross-Functional Collaboration: Partner with leaders across machine learning, data science, product engineering, and infrastructure to eliminate bottlenecks and deploy new solutions.
• Foundation Model Readiness: Architect compute and storage pipelines to manage millions of slides and complex derived artifacts without data fragmentation or synchronization latency.
• Inference Modernization: Modernize the AI product inference stack to support 5–10x growth in AI runs across global deployments.
• System Observability: Partner with Site Reliability Engineering (SRE) to establish comprehensive metrics covering compute utilization, network bottlenecks, and granular cost/turn-around-time attribution.
• Technology Refresh: Lead build-versus-buy audits and benchmark proprietary tools against leading commercial and open-source alternatives.

Qualifications

Required

• Bachelor’s or Master’s degree in Computer Science, Engineering, or a 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 growing engineering teams, managing team budgets/cloud costs, and driving MLOps platform adoption.
• Deep technical expertise with ML workloads on Kubernetes, cloud computing platforms (AWS/GCP/Azure), workflow orchestration (Airflow, Kubeflow, or proprietary equivalents), and DevOps principles/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.

Preferred

• Experience working with ML frameworks such as 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.