About This Role
This contract assignment with a banking client involves designing, building, and operating scalable machine learning platforms and production ML systems. The role focuses on owning the end-to-end ML lifecycle, from feature engineering and model deployment to monitoring, governance, and continuous retraining, and establishing enterprise MLOps capabilities.
Responsibilities
- Design, build, and optimize scalable, reusable ML platforms and production-grade ML systems that cover model development, training, validation, deployment, and monitoring
- Manage the end-to-end ML lifecycle, including model versioning, experiment tracking, reproducibility, and governance
- Build and maintain automated CI/CD/CT pipelines for model testing, validation, deployment, rollback, and retraining, using infrastructure-as-code and automated environment provisioning
- Design and support enterprise Feature Stores and a Model Registry to improve feature reuse, consistency, and model governance
- Partner with data engineering to build scalable training and inference data pipelines that integrate with data warehouses, data lakes, and operational sources, with strong data quality, lineage, and observability
- Develop frameworks for batch and real-time inference, including low-latency, event-driven scoring platforms built on streaming technologies
- Implement model monitoring and alerting for performance, data quality, data/concept/feature drift, and system health, with incident management and retraining strategies
- Optimize ML systems for performance, reliability, maintainability, and cost efficiency
- Implement MLOps governance and ensure compliance with security, privacy, risk, and regulatory requirements, including auditability, explainability, and documentation
- Collaborate with data scientists, product owners, and engineers to translate business needs into technical solutions, and mentor junior engineers while driving MLOps standards and best practices
Requirements
- 5+ years of experience in Machine Learning Engineering, MLOps, Data Engineering, or Software Engineering, including 3+ years building and supporting production ML systems
- Demonstrated experience implementing end-to-end ML lifecycle management: deployment and orchestration, experiment tracking, model versioning, automated retraining, and Responsible AI/model governance
- Hands-on experience operating ML workloads in the cloud (AWS SageMaker, Azure Machine Learning, or Google Vertex AI), supporting both batch and real-time solutions
- Strong proficiency in Python and SQL; Java, Scala
- Experience with Feature Store architecture and Model Registry implementation
- Experience with data platforms and transformation tools such as Snowflake, Databricks, IBM DB2 (or equivalent RDBMS), data lakes, dbt, Spark, PySpark, and Pandas, working with large-scale structured and unstructured data
