Description

You will build and maintain scalable MLOps infrastructure and deployment pipelines for machine learning training and inference.

This role is on-site.

Responsibilities

• Build MLOps pipelines and cloud solutions on AWS.
• Manage continuous deployment using GitHub Actions, Flux, and Kustomize.
• Containerize and deploy data science models using Docker and Kubernetes.
• Develop and deploy scalable tools for machine learning training and inference.
• Collaborate with data scientists, engineers, and architects to document processes.

Required Skills

• 6+ years of experience in MLOps.
• Strong proficiency with Kubernetes, Python, MongoDB, and AWS.
• Experience with Docker and container orchestration in cloud environments.
• Hands-on experience with Linux administration.
• Knowledge of ML models and Large Language Models (LLM).
• Experience building custom integrations between cloud systems using APIs.
• Understanding of software testing, benchmarking, and continuous integration.
• Ability to translate business needs into technical requirements.
• Experience with software development and test automation.

Preferred Skills

• Familiarity with MLOps frameworks like Kubeflow, MLFlow, DataRobot, or Airflow.
• Experience with data-oriented workflow orchestration such as Argo or Airflow.
• Knowledge of Apache SOLR.

Key Skills

Education

Any Graduate