Responsibilities
• Own the monitoring, tracking, and maintenance of ML models across Domino and SageMaker platforms.
• Implement MLflow for parameters, metrics, artifact management, and end to end lineage.
• Build and maintain scalable data pipelines for training, validation, and inference processes.
• Develop custom evaluation metrics, explainability components, and fairness/bias testing frameworks.
• Package models for deployment and support model lifecycle transitions across environments.
• Collaborate with data scientists, engineering teams, and governance stakeholders to ensure compliance and operational readiness.
Required Skills & Experience
• Strong experience with AWS and ML engineering
• Proficiency in Python and MLflow
• Hands on expertise with Domino and SageMaker SDKs
• Experience with feature engineering and scalable data pipelines
• Knowledge of model validation, explainability, and bias/fairness tooling
• Familiarity with Git based workflows, version control, and MLOps practices
Some of that data might live in relational systems, but it�s increasingly moving towards NoSQL systems and data lakes.
Skills:
This IT role requires a significant set of technical skills, including a deep knowledge of SQL, data modeling, and tools like Spark/Hive/Airflow.
Education/Work Exprerience:
1) Bachelor degree in Computer Science, Information Systems or related field
2) Post-graduate degree desired
3) Professional certification(s) desired 15+ years relevant experience
Never miss an opportunity! Create an alert based on the job you applied for.
