Description

You will design and implement cloud solutions and MLOps pipelines to support machine learning training and inference.

Responsibilities

• Build MLOps pipelines on AWS to support data science model containerization and deployment.
• Manage continuous deployment workflows using GitHub Actions, Flux, and Kustomize.
• Develop and deploy scalable tools and services for machine learning workloads.
• Collaborate with data scientists, data engineers, and architects to document processes.
• Create custom integrations between cloud-based systems using APIs.

Required Skills

• 6+ years of experience in MLOps.
• Strong proficiency with Kubernetes, Python, MongoDB, and AWS.
• Experience with Docker and containerization in cloud environments.
• Hands-on experience with Linux administration.
• Knowledge of ML models, LLMs, and VLLM.
• Experience with MLOps frameworks such as Kubeflow, MLFlow, DataRobot, or Airflow.
• Proficiency in software testing, benchmarking, and continuous integration.
• Understanding of Apache SOLR and database systems.
• Experience with software development and test automation.

Preferred Skills

• Familiarity with data-oriented workflow orchestration frameworks like Argo.

Key Skills

Education

Any Graduate