Description

You will build and maintain ML pipelines, automating model training, deployment, and monitoring across cloud environments.

This role is hybrid.

Responsibilities

• Develop and maintain ML pipelines using MLflow, Kubeflow, or Vertex AI.
• Automate model training, testing, deployment, and monitoring in GCP, AWS, or Azure.
• Implement CI/CD workflows for model lifecycle management, including versioning, monitoring, and retraining.
• Monitor model performance using observability tools and ensure compliance with model governance frameworks.
• Collaborate with engineering teams to provision containerized environments and support model scoring via low-latency APIs.

Required Skills

• 10+ years of professional experience in software engineering.
• 3+ years in AI/ML and machine learning model operations.
• Strong proficiency in Java and Python.
• Experience with SQL and ML libraries (scikit-learn, XGBoost, TensorFlow, PyTorch).
• Experience with cloud platforms (GCP, AWS, Azure) and containerization (Docker, Kubernetes).
• Familiarity with data engineering tools (Airflow, Spark) and MLOps frameworks.
• Solid understanding of software engineering principles and DevOps practices.

Preferred Skills

• Experience leveraging AutoML tools (Vertex AI AutoML, H2O Driverless AI) for rapid deployment.
• Ability to communicate complex technical concepts to non-technical stakeholders.

Key Skills

Education

Bachelor's degree