Description

You will own the ML infrastructure and drive the ML technical roadmap.

This role is hybrid.

Responsibilities

• Build MLOps pipelines for model experimentation, CI/CD, verification, validation, and monitoring.
• Collaborate with ML Engineers, Cloud Engineering, and Product Managers to develop tooling for training and production operations.
• Build and maintain data pipelines using Hadoop, Python, API Gateway, SageMaker, ECS/EKS, Lambda, Airflow, CloudWatch, and AWS CDK.
• Support and troubleshoot ML pipelines while continuously improving stability.
• Establish standards and practices for MLOps, including governance, compliance, and data security.

Required Skills

• 4+ years of overall engineering experience in distributed systems and data infrastructure.
• 3+ years' experience with AWS or other public cloud platforms (GCP, Azure).
• 2+ years of experience with ML infrastructure and ML DevOps.
• Experience building tooling and automation with ML engineers across the ML lifecycle.
• Experience with Kubernetes and ML CI/CD workflows.
• 3+ years' experience coding in Python.
• Experience with GCP, Azure, Hadoop, API Gateway, SageMaker, ECS, CI/CD, Airflow, CloudWatch.

Key Skills

Education

Any Gradute