Machine Learning Engineer

Join a growing tech-driven organisation on a mission to build scalable, production-grade machine learning systems. We're looking for a Machine Learning Engineer who enjoys hands-on development, thrives in cross-functional teams, and brings an engineering mindset to AI. This will be an initial 6-month contract with future extensions likely as the project is a multi year transformation.

What You’ll Do

As part of a high-performing AI/ML engineering team, your core responsibilities will include:

• Building and deploying ML-powered software solutions—exploring large datasets, experimenting with models, performing feature engineering, and scaling solutions for real-world usage.
• Designing and maintaining large-scale data and ML infrastructure to support end-to-end AI workflows.
• Writing clean, scalable, and modular code following best practices in software architecture and design.
• Developing internal tooling and frameworks to support ML projects (e.g., A/B testing, traceability, model versioning).
• Collaborating closely with data scientists, engineers, and product stakeholders in an agile setup to push forward innovative data-driven solutions.

What You Bring

• Degree in Computer Science, Engineering, or a related field—or equivalent hands-on experience.
• 4+ years of professional experience in machine learning engineering or applied ML roles.
• Proficient in Python and modern development practices (CI/CD, testing, modular architecture).
• Strong background in building and maintaining production-grade ML applications.
• Comfortable working with cloud-based ML platforms and services (any major provider; certification required within the first month if not already certified).
• Familiar with MLOps principles and tools (pipelines, model deployment, monitoring, etc.).
• Experience with large-scale data processing (batch/stream), data modelling, and storage technologies.
• Knowledge of ML frameworks and ability to select and apply the right tool for the problem.
• Exposure to agile development, experimentation, and data-driven decision-making.