ABOUT THE ROLE

We are looking for a Data / Machine Learning Engineer to join our growing engineering team. In this role, you will design, build, and operationalize data pipelines and ML systems that power our analytics and AI solutions. You should have a strong foundation in data engineering and hands-on experience taking machine learning models from experimentation to production - working closely with data scientists, engineers, and client-facing teams to deliver scalable, reliable, and impactful solutions.

What This Involves: 

• Developing a deep understanding of the business problems our analytical solutions are designed to solve. 
• Consulting continuously with client stakeholders, including IT teams, data owners, and business owners, to discover and map relevant existing data sources. 
• Collaborating with client IT teams to define the technical architecture for analytical solutions prior to deployment. 
• Building and maintaining data ingestion systems responsible for consolidating all necessary data sources into a centralised environment for analysis. 
• Designing and implementing end-to-end data analysis pipelines that transform raw data into business-ready outputs. 
• Integrating analytical results into business UIs developed in-house or into pre-existing client software systems. 
• Establishing scalable, efficient, and automated processes for data analysis, model development, validation, and deployment. 
• Working closely with data scientists and engineers to develop and ship new features in an iterative, continual-release environment. 
• Monitoring core infrastructure and proactively planning enhancements to support growing analytical demands. 
• Optimising model performance through rigorous testing, tracking, and iterative code refactoring. 
• Participate in technical discussions, architectural and code reviews, and continuous improvement initiatives.
• Adhere to internal and client-mandated data protection and compliance policies, ensuring all handling, storage, and sharing of data meets required security and privacy standards.

Requirements

• Relevant tertiary qualification in computer science, engineering, mathematics, or a related field. 
• 5+ years of data engineering experience, with at least 1 year in machine learning engineering. 
• Strong programming skills across SQL, Python, and at least one of Java or Scala. 
• Solid understanding of Linux, including basic system administration and shell scripting. 
• Hands-on experience with cloud platforms such as AWS, GCP, or Azure, including serverless development. 
• Experience building and managing data pipelines and workflow orchestration systems. 
• Familiarity with engineering best practices, including code reviews, unit testing, and version control. 
• Experience developing and deploying predictive models in production environments, with knowledge of MLOps and model integration into large-scale applications. 
• Proficiency with machine learning libraries such as Scikit-learn, XGBoost, MXNet, TensorFlow, or PyTorch.

Key Competencies

• Stakeholder Mentality: Treats the company's and client’s goals as their own and is genuinely motivated by its success.
• Organisational Excellence: Manages time and priorities effectively, ensuring tasks are completed accurately and on time even in a fast-paced environment.
• Discretion & Integrity: Handles sensitive and confidential information with professionalism and sound judgement.
• Problem Solving: Approaches challenges proactively and with a solution-oriented mindset, taking initiative rather than waiting to be directed.
• Collaboration: A team player who builds strong working relationships and communicates effectively with colleagues across all levels.

Why You Will Love It Here:

• Work on real-world AI and advanced analytics solutions with measurable business impact.
• Collaborate with a global team of engineers and data scientists.
• Exposure to diverse industries, modern cloud platforms, and cutting-edge AI technologies.
• A collaborative culture that values real outcomes.
• Rapid learning opportunities and diverse challenges.
• Flat organisational hierarchy with high visibility and accessibility to our leaders.