Description

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

Responsibilities

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

Required Skills

• 10+ years of professional experience in Software Engineering.
• 3+ years in AIML 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