Sr. Engineering Manager, MLOps

Responsibilities

• Define the MLOps Vision & Strategy: Architect a long-term roadmap that transitions ML workflows from manual scripts to a fully automated, self-service platform for all Quince Data Scientists and AI Researchers.
• Own the "Paved Road" for Production: Build and maintain the end-to-end infrastructure for model training, deployment, and serving, ensuring researchers can move from "idea to production" with zero friction.
• Drive Strategic Prioritization: Partner with business leaders to align infrastructure investments with core e-commerce drivers like real-time personalization, dynamic pricing, and inventory forecasting.
• Lead "Build vs. Buy" Evaluations: Make high-judgment decisions on when to leverage cloud-native services (e.g., SageMaker, Vertex AI) versus building custom internal tools to optimize for cost, speed, and flexibility.
• Guarantee System Scalability & Reliability: Oversee the uptime and performance of production ML services, ensuring the stack can handle massive traffic surges and seasonal spikes without degradation.
• Manage Compute Governance & Costs: Direct the optimization of high-cost computational resources, such as GPU clusters and cloud instances, balancing high-performance training needs with fiscal responsibility.
• Recruit and Mentor Top Talent: Build and lead a high-performing team of ML Infra and DevOps engineers, providing technical coaching, career pathing, and performance management.
• Establish MLOps Standards: Drive the adoption of best practices in CI/CD for ML, Infrastructure as Code (IaC), and automated testing to ensure a modular and maintainable system.
• Bridge the Research-Engineering Gap: Act as the primary cross-functional lead, translating the complex needs of AI Researchers into actionable engineering requirements for the infrastructure team.
• Define and Track Velocity Metrics: Establish KPIs for the infrastructure team, such as model deployment frequency, mean time to recovery (MTTR), and infrastructure cost per inference.
• Champion Operational Excellence: Lead root-cause analyses (RCAs) for production failures and foster a culture of accountability where systemic fixes are prioritized over "quick patches."
• Stay Ahead of the AI Curve: Monitor emerging trends in LLM-ops, vector databases, and real-time feature engineering to ensure Quince’s infrastructure remains competitive and future-proof.

Qualifications

• 10+ years of industry experience, with at least 3-5 years in a leadership or management role specifically focused on ML Infrastructure, MLOps, or large-scale Data Platform engineering.
• Proven track record of building and scaling MLOps platforms that support the full model lifecycle—from data ingestion and distributed training to real-time inference and monitoring.
• Deep technical expertise in cloud-native infrastructure (preferably AWS) and orchestration tools like Kubernetes (EKS), Docker, and Infrastructure as Code (Terraform/Pulumi).
• Hands-on experience with ML frameworks and tooling, such as PyTorch, TensorFlow, Kubeflow, or SageMaker, and a strong opinion on how to integrate them into a cohesive developer experience.
• Expertise in building and managing Feature Stores and high-throughput data pipelines (using tools like Spark, Flink, or Kafka) to ensure data consistency across training and serving.
• Experience partnering with AI Research and Data Science teams to understand their unique workflows and translate research needs into robust, scalable engineering solutions.
• Strong understanding of CI/CD for ML, including automated testing for models, model versioning, and "blue-green" or "canary" deployment strategies.
• Demonstrated ability to manage high-cost compute resources, with experience optimizing GPU utilization and cloud spend in a hyper-growth environment.
• Excellence in operational leadership, with a history of driving service availability, performance, and stability through rigorous on-call rotations and root-cause analysis.
• A product-oriented mindset, with the ability to treat infrastructure as a platform and prioritize the roadmap based on researcher velocity and business ROI.
• Exceptional communication and influence skills, capable of navigating ambiguity and building consensus across engineering, product, and data science leadership.
• Kindness and high standards: You move fast and push for excellence, but you do so as a supportive team player who fosters a culture of psychological safety and extreme candor.