Job Summary

• Bachelor’s degree in Information Technology, Computer Science, Software Development, Engineering, or a related field.
• Minimum 7 years of post-graduate experience in a data engineering or MLOps role.
• Minimum 4 years’ experience working with AWS cloud services or Databricks.
• Minimum AWS Solutions Architect Associate certification.
• AWS Machine Learning speciality certification is preferred.
• Proficiency in machine learning, data engineering, and cloud-based architectures.
• Excellent problem-solving skills and ability to work in a fast-paced environment.
• Strong communication and leadership skills.

Job Description

Role Overview:

• As a Senior MLOps Engineer, you will play a crucial role in the deployment and maintenance of machine learning and AI solutions developed by our data science team. This role requires a unique blend of deep technical knowledge and practical experience in data engineering, machine learning operations (MLOps), and AWS cloud services, along with strong interpersonal, problem-solving and leadership skills.

Key Responsibilities:

• Design, implement and maintain MLOPs solutions on cloud (AWSDatabricks)
• Implement and manage MLOps pipelines to streamline the deployment of machine learning models in cloud (AWSDatabricks).
• Develop and maintain reusable feature stores, robust data architectures, and efficient data engineering practices.
• Data science model review, code refactoring and optimization, containerization, deployment, versioning, and monitoring of its quality
• Work closely with risk and governance teams to ensure compliance and security in cloud environments (AWS/Databricks).
• Establish and enforce best practices and standards for MLOps within the cluster.
• Provide technical leadership and coaching to junior team members.
• Coordinate cross-functionally with various technical teams to facilitate the integration of AI solutions into business processes.
• Continuously monitor and optimize the performance of deployed machine learning solutions
• Meaningfully contribute to & ensure solutions align to the design & direction of the group architecture, cloud governance, data standards, principles, preferences & practices. Short term deployment must align to strategic long-term delivery.

Technical skills:

• Strong understanding of MLOps practices and principles, including CI/CD pipelines, version control, and model deployment.
• Ability to design and implement MLOps pipelines in cloud (AWSDatabricks)
• Expertise in machine learning algorithms (frameworks such as scikit-learn, Keras, PyTorch, Tensorflow),
• Proficiency in programming languages such as Python, PySpark, Spark
• Familiarity with big data technologies and frameworks (e.g., Hadoop, Spark).
• Hands-on experience with AWS services for data processing such as AWS Glue, AWS Lambda, Amazon S3 and Amazon SageMaker.
• Proficiency in AWS management and deployment tools, including AWS CloudFormation, AWS CLI, AWS CodePipeline, AWS CodeBuild, AWS CodeDeploy, Amazon ECS, Amazon EKS, AWS Step Functions and Github
• Experience in designing and implementing scalable data architectures.
• Familiarity with building and maintaining feature stores.
• Knowledge of database management, SQL, ETL processes and data warehousing principles.
• Ability to translate complex technical concepts into understandable terms for non-technical stakeholders.
• Understanding of data security, privacy, and compliance standards relevant to the banking industry.
• Experience coordinating with risk and governance teams to ensure secure and compliant solutions.
• Strong communication skills for effectively collaborating with cross-functional teams and mentoring junior staff.

Minimum Requirements:

• Bachelor’s degree in Information Technology, Computer Science, Software Development, Engineering, or a related field.
• Minimum 7 years of post-graduate experience in a data engineering or MLOps role.
• Minimum 4 years’ experience working with AWS/Databricks cloud services.
• Minimum AWS Solutions Architect Associate certification.
• AWS Machine Learning specialty certification is preferred.
• Proficiency in machine learning, data engineering, and cloud-based architectures.
• Excellent problem-solving skills and ability to work in a fast-paced environment.
• Strong communication and leadership skills.

Additional Job Description

• Ensure that all activities and duties are carried out in full compliance with regulatory requirements, Enterprise Wide Risk Management Framework and Absa Policies and Policy Standards.
• Understand and manage risks and risk events (incidents) relevant to the role.

Education

• Bachelor’s Degree: Information Technology