Description

You will lead the operationalization of Machine Learning workloads by designing, building, and maintaining the necessary infrastructure.

Responsibilities

• Architect scalable, cost-efficient, and secure MLOps solutions.
• Design, implement, and deploy MLOps solutions within AWS.
• Implement CI/CD pipelines for ML applications to ensure smooth development and deployment.
• Collaborate with data scientists on model serving, versioning, and reproducibility requirements.
• Monitor and optimize model performance in production, resolving issues proactively.
• Automate repetitive tasks across MLOps workflows to improve efficiency.

Required Skills

• 10+ years of experience in MLOps with proven deployment track record.
• In-depth working knowledge of MLflow and Kubeflow.
• Expertise in AWS and Infrastructure as Code (IaC) principles.
• Strong experience with DevOps methodologies and CI/CD pipelines, including Github Actions.
• In-depth knowledge of Kubernetes and Docker.
• Strong programming skills in Python.
• Experience with ML frameworks such as TensorFlow and PyTorch.
• Understanding of the machine learning lifecycle, from training to deployment.
• Experience with SageMaker.

Key Skills

Education

Any Graduate