The MLOps Engineering Manager is an engineering role responsible for the infrastructure, automation, and pipelines that power both Data Engineering and Machine Learning operations. This role leads the design, deployment, and maintenance of scalable data platforms (e.g., Hive Metastores, PostgreSQL, MinIO/S3 architectures) and robust MLOps lifecycles.

The Manager ensures that raw digital banking data flows seamlessly to the BI/Data Science teams, and that predictive models are deployed into production via automated, low-latency, and secure CI/CD pipelines.

Responsibilities or duties

Infrastructure Architecture & Data Engineering Leadership.

• Oversee the architecture and optimization of the department’s persistent data platforms, including distributed object storage, Hive Metastores, and relational databases.
• Work with the Senior Data Engineer to ensure that robust, containerized data pipelines (e.g., PySpark workflows running on Docker/Kubernetes) are optimized for low-latency transaction processing.
• Manage cluster resources, storage scaling, and compute environments to balance performance with infrastructure costs.

MLOps Strategy & Automated CI/CD Lifecycles.

• Design and enforce automated CI/CD pipelines for deploying Machine Learning models as highly available, production-grade microservices/APIs.
• Implement automated infrastructure to track model telemetry, monitoring API latency, prediction accuracy, and data/concept drift in real-time.
• Own the orchestration and containerization strategy (Docker, Kubernetes) to guarantee that environments match perfectly from a data scientist’s local sandbox to the production cluster.
• Act as the critical operational bridge linking the Data Science Manager (who designs the models) and the BI Manager (who consumes the data assets) with the bank’s core Core Banking IT and Security teams.

Qualifications or requirements (e.g., education, skills)

• Bachelor’s in Computer Science, Information Technology, Software Engineering, Data Engineering, or a related technical field.
• Proficiency with Docker and Kubernetes for scaling data and model workloads.
• Strong hands-on experience setting up, tuning, and decoupling data architectures using Hive Metastores, PostgreSQL, and high-performance object storage.
• Deep familiarity with modern deployment and tracking pipelines (e.g., Git Actions or cloud-native equivalents etc.).
• Strong mastery of Python, shell scripting, and distributed processing environments using PySpark.
• The ability to design modular, decoupled data architectures that prevent systemic bottlenecks.
• A strong “infrastructure as code” and automation-first mindset; a refusal to accept manual, fragile deployment steps.
• Ability to negotiate infrastructure access, security clearances, and server resource allocations with centralized corporate IT divisions.
• Flexible and adoptive to market dynamics and experimentation.
• Customer‑centric mindset.
• Self-driven and problem‑solving skills.

Experience needed

Minimum of 5 years of experience spanning DevOps, Cloud/Infrastructure Engineering, Data Engineering, or MLOps with at least 1–2 years in a lead or managerial position.

• Oversee the architecture and optimization of the department’s persistent data platforms, including distributed object storage, Hive Metastores, and relational databases.
• Work with the Senior Data Engineer to ensure that robust, containerized data pipelines (e.g., PySpark workflows running on Docker/Kubernetes) are optimized for low-latency transaction processing.
• Manage cluster resources, storage scaling, and compute environments to balance performance with infrastructure costs.
• Design and enforce automated CI/CD pipelines for deploying Machine Learning models as highly available, production-grade microservices/APIs.
• Implement automated infrastructure to track model telemetry, monitoring API latency, prediction accuracy, and data/concept drift in real-time.
• Own the orchestration and containerization strategy (Docker, Kubernetes) to guarantee that environments match perfectly from a data scientist’s local sandbox to the production cluster.
• Act as the critical operational bridge linking the Data Science Manager (who designs the models) and the BI Manager (who consumes the data assets) with the bank’s core Core Banking IT and Security teams.

• Proficiency with Docker and Kubernetes for scaling data and model workloads.
• Strong hands-on experience setting up, tuning, and decoupling data architectures using Hive Metastores, PostgreSQL, and high-performance object storage.
• Deep familiarity with modern deployment and tracking pipelines (e.g., Git Actions or cloud-native equivalents etc.).
• Strong mastery of Python, shell scripting, and distributed processing environments using PySpark.
• The ability to design modular, decoupled data architectures that prevent systemic bottlenecks.
• A strong “infrastructure as code” and automation-first mindset; a refusal to accept manual, fragile deployment steps.
• Ability to negotiate infrastructure access, security clearances, and server resource allocations with centralized corporate IT divisions.
• Flexible and adoptive to market dynamics and experimentation.
• Customer‑centric mindset.
• Self-driven and problem‑solving skills.

• Bachelor’s in Computer Science, Information Technology, Software Engineering, Data Engineering, or a related technical field.