Responsibilities

• Build & Automate ML Pipelines: Design, implement, and maintain CI/CD pipelines for machine learning models, ensuring automated data ingestion, model training, testing, versioning, and deployment.
• Operationalize Models: Collaborate closely with data scientists to containerize, optimize, and deploy their models to production, focusing on reproducibility, scalability, and performance.
• Infrastructure Management: Design and manage the underlying cloud infrastructure (AWS) that powers our MLOps platform, leveraging Infrastructure-as-Code (IaC) tools to ensure consistency and cost optimization.
• Monitoring & Observability: Implement comprehensive monitoring, alerting, and logging solutions to track model performance, data integrity, and pipeline health in real-time. Proactively address issues like model or data drift.
• Governance & Security: Establish and enforce best practices for model and data versioning, auditability, security, and access control across the entire machine learning lifecycle.
• Tooling & Frameworks: Develop and maintain reusable tools and frameworks to accelerate the ML development process and empower data science teams.

Required Qualifications

• Experience: Overall 10+ years of experience with 4+ years of experience in MLOps, Machine Learning Engineering, or a related DevOps role with a focus on ML workflows.
• Cloud Expertise: Extensive hands-on experience in designing and implementing MLOps solutions on AWS. Proficient with core services like SageMaker, S3, ECS, EKS, Lambda, SQS, SNS, and IAM.
• Coding & Automation: Strong coding proficiency in Python. Extensive experience with automation tools, including Terraform for IaC and GitHub Actions.
• MLOps & DevOps: A solid understanding of MLOps and DevOps principles. Hands-on experience with MLOps frameworks like Sagemaker Pipelines, Model Registry, Weights and Bias, MLflow or Kubeflow and orchestration tools like Airflow or Argo Workflows.
• Containerization: Expertise in developing and deploying containerized applications using Docker and orchestrating them with ECS and EKS.
• Model Lifecycle: Experience with model testing, validation, and performance monitoring. Good understanding of ML frameworks like PyTorch or TensorFlow is required to effectively collaborate with data scientists.
• Communication: Excellent communication and documentation skills, with a proven ability to collaborate with cross-functional teams (data scientists, data engineers, and architects).