Description

You will own the end-to-end lifecycle of machine learning models, from pipeline development to monitoring and governance in cloud environments.

This role is on-site.

Responsibilities

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

Required Skills

• 10+ years of professional software engineering experience.
• 3+ years of experience in AI/ML and Machine Learning Model Operations.
• Strong proficiency in Java, Python, and SQL.
• Experience with ML libraries such as scikit-learn, XGBoost, TensorFlow, or PyTorch.
• Hands-on experience with cloud platforms (GCP, AWS, Azure) and containerization (Docker, Kubernetes).
• Familiarity with data engineering tools like Airflow and Spark.
• Solid understanding of software engineering principles and DevOps practices.

Preferred Skills

• Experience leveraging AutoML tools (e.g., 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