Description

You will own the reliability, deployment, and scaling of machine learning and data services.

This role is on-site.

Responsibilities

• Develop and deploy scalable tools for ML training and inference.
• Build MLOps pipelines on AWS and implement cloud solutions.
• Automate continuous deployment using GitHub Actions and Flux.
• Document operational processes and system designs.
• Communicate technical requirements between data scientists, engineers, and architects.

Required Skills

• 8+ years of professional experience in MLOps environments.
• Expert proficiency with Kubernetes and Docker.
• Strong command of Python and Linux administration.
• Experience designing cloud solutions using AWS.
• Familiarity with MongoDB and Microservices architecture.
• Ability to build custom integrations between cloud systems via APIs.
• Experience with CI/CD practices and test automation.
• Knowledge of ML models, LLMs, and data-oriented workflow orchestration (e.g., Airflow).
• Experience with Site Reliability principles and Platform engineering.

Key Skills

Education

Any Gradute