Description

You will bridge the gap between data science and production by building and managing the infrastructure required for AI/ML lifecycles.

This role is on-site.

Responsibilities

• Develop end-to-end Data, Dev, and MLOps pipelines to ensure analytics solutions are delivered predictably and sustainably.
• Build and maintain data pipelines for analytics, model evaluation, and training, including versioning and compliance.
• Improve and maintain automated CI/CD pipelines to support continuous integration and deployment.
• Assist data scientists with model training, evaluation, and validation.
• Collaborate with product owners and architects to build technical roadmaps and align with target architectures.

Required Skills

• 5+ years of IT experience in software development and Infrastructure as Code.
• Expertise in container technologies, specifically Docker and Kubernetes.
• Proficiency in AWS services including EC2, S3, Lambda, Step Functions, Glue, and EMR.
• Experience with AWS Fargate and managing AWS database environments.
• Strong background in DevOps, CI/CD, and Microservices architectures.
• Experience developing and managing packages and APIs using Python.
• Solid understanding of machine learning fundamentals.

Key Skills

Education

Any Gradute