Machine Learning Engineer

• 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.

• 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.
• Experience building CI/CD pipelines for ML workloads using Git/GitHub/GitLab and Jenkins, GitHub Actions, Azure DevOps, or equivalent, plus Terraform or other infrastructure-as-code tools.
• Experience with Docker and Kubernetes (OpenShift preferred), plus Linux administration and shell scripting.
• Experience with streaming and event-driven architectures (Apache Kafka) and real-time inference pipelines.
• Experience with monitoring and observability tools such as Prometheus, Grafana, the ELK Stack, or cloud-native monitoring. Experience in regulated industries is highly desirable.

Preferred Skills

• Experience implementing enterprise Feature Stores and designing Model Registry and ML governance frameworks.
• Experience building real-time decisioning or scoring platforms for use cases such as credit risk, fraud, customer analytics, or recommendation systems.
• Familiarity with MLflow, Kubeflow, Airflow, Argo Workflows, Feast, SageMaker Pipelines, or similar platforms.
• Knowledge of distributed systems and software architecture patterns.
• Familiarity with AI/ML regulatory frameworks and model risk management practices.
• Relevant cloud certifications (AWS, Azure, GCP).

• Competitive hourly rate
• A collaborative and dynamic work environment
• The opportunity to work on cutting-edge projects with a leading banking client