Description

You will lead the engineering effort to bridge data science and production systems, ensuring ML models deploy, are monitored, and scale effectively.

This role is hybrid.

Responsibilities

• Deploy ML models into production environments using Docker, Kubernetes, and serving frameworks.
• Implement CI/CD pipelines for end-to-end machine learning workflows.
• Build and maintain automated pipelines for data ingestion, training, validation, and monitoring.
• Set up monitoring and alerting for model performance, data drift, and infrastructure health.
• Collaborate with data scientists to transition models from experimentation to production systems.

Required Skills

• 10+ years of professional experience in ML engineering or MLOps.
• Expert proficiency with MLOps practices and CI/CD pipelines.
• Strong command of AWS cloud infrastructure.
• Proficiency in Python for ML workflows.
• Experience with TensorFlow for model development.
• Experience with Terraform for infrastructure as code.
• Familiarity with model serving frameworks (e.g., MLflow).
• Ability to manage role-based access control (RBAC) for ML infrastructure.

Key Skills

Education

Any Graduate