Job purpose

The Machine Learning Engineer is 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.

Principal 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, pre-processing, 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.

Qualification required

• Bachelors degree in Computer Science, Data Science, Software Engineering, or a related technical field.
• Minimum of 3 years of experience in machine learning engineering, data engineering, or related roles, with proven experience deploying and maintaining ML models in production environments.
• Proficiency in Python and Java for ML model development and deployment, with expertise in ML frameworks such as TensorFlow, PyTorch, and Scikit-learn.
• Practical knowledge of API development, DevOps tools, and container orchestration, including strong experience with containerization and orchestration tools (Docker, Kubernetes).
• Familiarity with MLOps pipelines, model monitoring, and version control systems (Git), as well as knowledge of CI/CD pipelines and DevOps practices for ML deployment.
• Hands-on experience with data preprocessing, feature engineering, and ETL pipelines.
• Understanding of model performance monitoring, API development, and security best practices, with familiarity with cloud or hybrid deployment environments (Azure, on-prem).
• Strong analytical and problem-solving skills, with excellent collaboration and teamwork with cross-functional teams (Data Science, MLOps, and IT).
• Good communication and documentation skills, attention to detail and focus on production quality, and adaptability to fast-paced, technology-driven environments.
• 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, pre-processing, 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.

• Python
• Java
• TensorFlow
• PyTorch
• Scikit-learn
• API development
• DevOps tools
• Container orchestration
• Docker
• Kubernetes
• MLOps pipelines
• Model monitoring
• Version control systems (Git)
• CI/CD pipelines
• Data preprocessing
• Feature engineering
• ETL pipelines
• Cloud or hybrid deployment environments (Azure, on-prem)

• Bachelors degree in Computer Science, Data Science, Software Engineering, or a related technical field.
• Minimum of 3 years of experience in machine learning engineering, data engineering, or related roles, with proven experience deploying and maintaining ML models in production environments.
• Proficiency in Python and Java for ML model development and deployment, with expertise in ML frameworks such as TensorFlow, PyTorch, and Scikit-learn.
• Practical knowledge of API development, DevOps tools, and container orchestration, including strong experience with containerization and orchestration tools (Docker, Kubernetes).
• Familiarity with MLOps pipelines, model monitoring, and version control systems (Git), as well as knowledge of CI/CD pipelines and DevOps practices for ML deployment.
• Hands-on experience with data preprocessing, feature engineering, and ETL pipelines.
• Understanding of model performance monitoring, API development, and security best practices, with familiarity with cloud or hybrid deployment environments (Azure, on-prem).
• Strong analytical and problem-solving skills, with excellent collaboration and teamwork with cross-functional teams (Data Science, MLOps, and IT).
• Good communication and documentation skills, attention to detail and focus on production quality, and adaptability to fast-paced, technology-driven environments.
• Experience working in financial institutions or regulated environments is an added advantage.