Key Outcomes may include but are not limited to:

Machine Learning Automation and Software Engineering:

• Design, build, and deploy machine learning and analytics automation processes.
• Refactor existing code bases to enhance efficiency, robustness (in terms of testing, code quality), scalability and automation of machine learning workflows.

Cloud-Native Development:

 

• Utilize Databricks and Azure as a primary platform for data engineering and machine learning use cases.
• Work within the Azure ecosystem, leveraging services such as Azure Functions, CosmosDB, API Gatway, Azure Machine Learning, etc. to build intelligent data applications.

DevOps, Version Control, and Software Engineering:

 

• Build out CI/CD pipelines to improve development and deployment practices. Hands-on experience with Git and DevOps tools such as Jenkins and Azure DevOps is required.
• Build robust testing and monitoring capabilities for machine learning and AI use cases.
• Knowledge of Terraform is advantageous.
• Experience building APIs to serve machine learning models.
• Apply software engineering best practices to develop robust, scalable, and maintainable code.
• Create microservice applications using Docker and container orchestration tools like OpenShift.
• Collaborate with cross-functional teams to deliver high-quality solutions software solutions for machine learning and data use cases.
• Create and maintain documentation of processes, technologies, and code bases.

Machine Learning Frameworks:

• Familiarity with machine learning frameworks/packages such as MLFlow, PyTorch, Tensorflow, etc is beneficial for the productionization of machine learning use cases.

Collaboration:

• Collaborate closely with data scientists, actuaries, data engineers, and other data software engineers to understand and address their data needs.
• Contribute actively to the architecting of our bank’s modern Machine Learning data ecosystem.

Education and Experience: 

• At least 1-3 years’ working experience as a Software Engineer.
• Bachelor’s degree in engineering or a related field. Other qualifications will be considered if accompanied by sufficient experience in software engineering.

Technical skills or knowledge: 

 

• 2-years’ experience using Python, SQL.
• Exposure to Linux shell scripting advantageous.
• Experience with Spark is advantageous.
• Experience and interest in software architecture.
• Knowledge of cloud compute services.
• Familiarity with serverless computing and cloud-native development.
• Keen interest in systems design and software architecture.
• Knowledge of machine learning frameworks/packages (e.g MLFlow, Spark ML, Sklearn etc).
• Understanding of CI/CD concepts and API development, implementation experience advantageous. 
• Strong critical thinking, problem-solving and collaboration skills.
• Ability to collaborate with cross-functional tech teams as well as business/product teams.
• Excellent communication skills.
• Commitment to excellence and high-quality delivery.
• Passion for personal development and growth, with a high learning potential.