Roles and Responsibilities

• Build and maintain continuous deployment pipelines using GitHub Actions, Flux, and Kustomize.
• Design and implement scalable cloud-based MLOps solutions on AWS.
• Containerize and deploy data science and machine learning models using Docker, VLLM, and Kubernetes.
• Collaborate effectively with data scientists, data engineers, and solution architects; document processes and system designs.
• Develop and deploy scalable tools and services for training and inference of machine learning models.
• Apply knowledge of machine learning models, including large language models (LLMs), in production environments.

Qualifications

• 6+ years of experience in MLOps or related roles, with strong expertise in Kubernetes, Python, MongoDB, and AWS.
• Proficiency in Linux system administration.
• Solid understanding of Apache Solr.
• Hands-on experience with containerization and orchestration using Docker and Kubernetes in cloud environments.
• Experience building and maintaining MLOps pipelines using frameworks like Kubeflow, MLflow, DataRobot, or Airflow.
• Familiarity with workflow orchestration tools such as Argo, Airflow, or Kubeflow Pipelines.
• Experience in developing custom cloud integrations using APIs.
• Knowledge of machine learning methodologies, best practices, and model lifecycle management.
• Proven ability to develop and maintain machine learning systems using open-source tools.
• Understanding of the tools and workflows used by data scientists, with experience in test automation and CI/CD practices.
• Strong software testing, benchmarking, and continuous integration skills.
• Ability to translate business requirements into scalable technical solutions.

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