Description

You will own the end-to-end lifecycle of machine learning models in production.

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, retraining, and lifecycle management.
• Monitor model performance with observability tools and ensure governance compliance including documentation and explainability.
• Collaborate with engineering to provision containerized environments for low-latency model scoring APIs.

Required Skills

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

Key Skills

Education

Any Gradute