Ml Ops Engineer 4

About Costco Wholesale Costco Wholesale is a multi-billion-dollar global retailer with warehouse club operations in eleven countries. They provide a wide selection of quality merchandise, plus the convenience of specialty departments and exclusive member services, all designed to make shopping a pleasurable experience for their members. About Costco Wholesale India At Costco Wholesale India, we foster a collaborative space, working to support Costco Wholesale in developing innovative solutions that improve members’ experiences and make employees’ jobs easier. Our employees play a key role in driving and delivering innovation to establish IT as a core competitive advantage for Costco Wholesale. Position Title: ML Ops Engineer 4 Job Description: Roles & Responsibilities: - Define the long-term vision and strategy for MLOps initiatives: Set the direction for the organization’s MLOps, model deployment, and monitoring practices. - Lead and manage a team of MLOps engineers: Provide technical guidance, mentorship, and career development for team members. - Identify and explore cutting-edge research areas and technologies: Stay abreast of the latest advancements in MLOps, model serving, and AI operations. - Drive innovation and the development of novel MLOps solutions: Lead efforts, prototype new approaches, and oversee implementation of advanced MLOps platforms. - Design and manage scalable ML infrastructure and pipelines on GCP; oversee model deployment (A/B testing, rollouts/rollbacks, auto-scaling), and establish monitoring/observability (performance, drift, KPIs). - Ensure ML operations meet governance, security, compliance, and disaster recovery standards across the organization. - Collaborate with executive leadership on strategic decision-making: Align MLOps initiatives with business objectives and organizational priorities. - Establish and enforce MLOps standards and best practices: Ensure quality, reproducibility, and security of ML systems across the organization. - Represent the organization in external MLOps communities: Speak at conferences, publish thought leadership, and build partnerships with academia and industry. Technical Skills: - 12+ - years of experience - Mastery of relevant technical skills: Deep expertise in MLOps, model deployment, monitoring, and governance. - Significant experience in designing and implementing complex MLOps systems at scale: Lead the architecture and deployment of large-scale MLOps platforms on GCP. - Hands-on experience architecting large-scale ML platforms on GCP (Vertex AI, GKE, Dataflow, Big Query, Pub/Sub, Cloud Composer), implementing experiment tracking (MLflow, Weights & Biases, TensorBoard), feature stores (Vertex AI), data pipelines and workflow orchestration, and ensuring cloud security, compliance, disaster recovery, and cost optimization. - Strong leadership and team management skills: Build, mentor, and lead high-performing MLOps teams. - Excellent strategic thinking and problem-solving abilities: Translate business challenges into scalable, reliable MLOps solutions. - Exceptional communication and influencing skills: Advocate for MLOps initiatives, and influence executive decisions and represent the organization externally through conferences, publications, and industry engagement. Must Have Skills: - Deep expertise in MLOps, model deployment, monitoring, and governance - Experience building scalable MLOps platforms on GCP - Proficiency with CI/CD for ML, containerization (e.G. Docker, Kubernetes), IaC (Terraform), and orchestration - Leadership in MLOps strategy, standards, and cross-team collaboration - Hands-on expertise with GCP ML and data services (Vertex AI, Dataflow, BigQuery, Pub/Sub, Cloud Composer, GKE). - Experience implementing model observability (performance monitoring, drift detection, dashboards, and alerts). - Proficiency with experiment tracking (MLflow, W&B) and feature store management. - Knowledge of cloud security, compliance, and cost optimization strategies.