Description

You will design and maintain the infrastructure for machine learning model deployment, monitoring, and lifecycle management.

This role is remote.

Responsibilities

• Build and optimize CI/CD pipelines for ML models using GitHub Actions, Jenkins, GitLab CI, or Azure DevOps.
• Containerize applications and orchestrate deployments using Docker and Kubernetes.
• Implement model monitoring, governance, and auditability in production environments.
• Automate infrastructure and data pipelines on AWS, Azure, or GCP.
• Collaborate with engineers to integrate Python-based ML solutions into scalable cloud architectures.

Required Skills

• 5+ years of experience in MLOps or Machine Learning Engineering.
• Strong proficiency in Python and core ML libraries (TensorFlow, PyTorch, Scikit-learn).
• Hands-on experience with MLOps frameworks such as MLflow, Kubeflow, Airflow, SageMaker, Vertex AI, or Azure Machine Learning.
• Proven track record with AWS, Azure, or GCP cloud platforms.
• Expertise in containerization (Docker) and orchestration (Kubernetes).
• Experience with Linux, Git, and infrastructure automation.

Preferred Skills

• Experience in Healthcare, Life Sciences, Genomics, or Oncology domains.
• Background in Bioinformatics or deploying AI/ML solutions in regulated environments.

Key Skills

Education

Bachelor's degree