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
- Focused on manipulating data in a software engineering capacity.
- Some of that data might live in relational systems, but its increasingly moving towards NoSQL systems and data lakes.
- Normalize databases and ascertain the structure of the data meets the requirements of the applications that are accessing the information.
- Construct datasets that are easy to analyze and support company requirements.
- Combine raw information from different sources to create consistent and machine-readable formats. 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's degree in computer science, Information Systems or related field
2) Post-graduate degree desired
3) Professional certification(s) desired