AWS ML Ops Lead — SageMaker Pipelines & Feature Store

This role requires hands-on experience building end-to-end machine learning pipelines on AWS, specifically leveraging these AWS services:

• Feature Store
• Please prioritize candidates who can both speak to and demonstrate practical implementation of these AWS services as part of ML model deployment pipelines.
• 6+ years of deep ML Ops experience, with strong expertise in AWS Sage Maker services.
• Lead, design, and implement ML Ops infrastructure by building and maintaining scalable, secure, and automated pipelines for model and data deployment across multiple environments.
• Establish and enforce ML Ops best practices that align with the client’s existing infrastructure, architecture patterns, and model deployment needs.
• Observability & Monitoring:
  Implement robust monitoring, logging, and alerting systems for deployed agents and models to ensure operational visibility, reliability, and performance.
• Lifecycle Management:
  Oversee the full model lifecycle, including feature store/table management, model registry and governance, evaluation workflows, deployment testing, and inference.
• Collaboration:
  
  Work closely with data scientists, AWS platform engineers, and product/platform teams to embed ML Ops best practices into development and deployment workflows.
• Security & Compliance:
  Ensure all ML Ops processes adhere to security standards, data governance policies, and regulatory requirements.
• Continuous Improvement:
  Drive automation, optimization, and modernization initiatives to enhance the efficiency, scalability, and resilience of ML Ops systems.