Job Purpose

Responsible for designing, building, optimizing, and deploying machine learning (ML) models into production environments. The role bridges the gap between data science and operational systems by ensuring that models are scalable, secure, and performant.

Principle Responsibilities

• Develop, deploy, and operationalize machine learning and deep learning models in collaboration with data scientists and MLOps engineers.
• Build and maintain model APIs, microservices, and serving infrastructures for real-time and batch inference.
• Ensure model scalability, performance, and security in production environments.
• Implement CI/CD pipelines for seamless integration, testing, and deployment of ML models.
• Collaborate with data engineers to optimize data ingestion, preprocessing, and feature engineering for model readiness.
• Monitor deployed models for drift, accuracy degradation, and operational performance, implementing retraining pipelines as needed.
• Contribute to the design and maintenance of ML infrastructure and containerized environments using tools like Docker and Kubernetes.
• Work closely with the Data Governance and AI Governance teams to ensure compliance with ethical, regulatory, and operational standards.
• Document ML processes, model configurations, and versioning for auditability and reproducibility.
• Continuously research and integrate emerging ML engineering practices and tools to enhance model lifecycle management.

Qualifications Required

• Bachelor’s degree in Computer Science, Data Science, Software Engineering, or a related technical field.
• 3–5 years of experience in machine learning engineering, data engineering, or related roles.
• Proven experience deploying and maintaining ML models in production environments.
• Practical knowledge of API development, DevOps tools, and container orchestration.
• Familiarity with MLOps pipelines, model monitoring, and version control systems (Git).
• Experience working in financial institutions or regulated environments is an added advantage.

• Develop, deploy, and operationalize machine learning and deep learning models in collaboration with data scientists and MLOps engineers.
• Build and maintain model APIs, microservices, and serving infrastructures for real-time and batch inference.
• Ensure model scalability, performance, and security in production environments.
• Implement CI/CD pipelines for seamless integration, testing, and deployment of ML models.
• Collaborate with data engineers to optimize data ingestion, preprocessing, and feature engineering for model readiness.
• Monitor deployed models for drift, accuracy degradation, and operational performance, implementing retraining pipelines as needed.
• Contribute to the design and maintenance of ML infrastructure and containerized environments using tools like Docker and Kubernetes.
• Work closely with the Data Governance and AI Governance teams to ensure compliance with ethical, regulatory, and operational standards.
• Document ML processes, model configurations, and versioning for auditability and reproducibility.
• Continuously research and integrate emerging ML engineering practices and tools to enhance model lifecycle management.

• API development
• DevOps tools
• Container orchestration (Docker, Kubernetes)
• MLOps pipelines
• Model monitoring
• Version control systems (Git)

• Bachelor’s degree in Computer Science, Data Science, Software Engineering, or a related technical field.
• 3–5 years of experience in machine learning engineering, data engineering, or related roles.
• Proven experience deploying and maintaining ML models in production environments.
• Practical knowledge of API development, DevOps tools, and container orchestration.
• Familiarity with MLOps pipelines, model monitoring, and version control systems (Git).
• Experience working in financial institutions or regulated environments is an added advantage.