Machine Learning Engineer

Introduction

We are seeking a Machine Learning Engineer to join an Enterprise Data Science / Group Technology environment. The successful candidate will be responsible for taking machine learning and data science solutions from development through to production.

The role requires a strong combination of Data Science, Machine Learning, Software Engineering, DevOps and Platform Engineering capabilities.

The ideal candidate will understand machine learning concepts and data science applications while also having hands-on experience with Kubernetes, Azure Kubernetes Service (AKS), Databricks, CI/CD, automation and production deployment environments.

Duties & Responsibilities

RESPONSIBILITIES

• Develop, deploy and maintain machine learning and data science solutions in production environments.
• Work across data science, software engineering and platform engineering teams.
• Design and implement production-ready machine learning solutions.
• Develop and maintain automated deployment and CI/CD pipelines.
• Deploy and manage applications and machine learning workloads using Kubernetes and Azure Kubernetes Service (AKS).
• Work with Databricks for data engineering, data science and machine learning workloads.
• Develop software and automation using strong programming and software engineering practices.
• Build and integrate APIs and production services.
• Support deployment, infrastructure and platform requirements for machine learning applications.
• Implement DevOps and MLOps practices across the machine learning lifecycle.
• Support model deployment, monitoring and operationalisation.
• Work with data ingestion and real-time data pipelines.
• Collaborate with Data Scientists, Software Engineers, Platform Engineers, DevOps Engineers and other technical stakeholders.
• Troubleshoot and improve production machine learning systems.
• Contribute to system design and scalable production architectures.

Essential Requirements

Data Science & Machine Learning

• Strong understanding of Data Science and Machine Learning concepts.
• Understanding of machine learning models and how data science applications are developed and operationalised.
• Practical experience working with machine learning solutions.

Platform Engineering

Hands-on experience with:

• Kubernetes
• Azure Kubernetes Service (AKS)
• Databricks
• Cloud/platform environments
• Infrastructure and deployment environments
• Application and workload deployment

Software Engineering & Production Deployment

• Strong Software Engineering background.
• Strong programming experience.
• Experience taking applications, data science solutions or machine learning models into production.
• Experience with CI/CD.
• Experience with DevOps practices.
• Experience developing and maintaining automation and deployment pipelines.
• Experience working with APIs.
• Understanding of production environments and deployment processes. Advantageous Skills

  Experience with any of the following will be advantageous:

• Python
• Docker
• MLOps
• MLflow
• JupyterHub
• Model monitoring
• Data ingestion pipelines
• Real-time data pipelines
• System design
• Feature stores
• Microsoft Azure
• Cloud-native technologies
• Machine learning model lifecycle management
• Additional technologies can be learned where the candidate demonstrates a strong core foundation in Machine Learning, Software Engineering and Platform Engineering.

  REQUIRED EXPERIENCE

• Approximately 4–7 years of relevant professional experience.
• Demonstrated experience delivering technical solutions into production.
• Experience working with machine learning or data science applications.
• Experience working with Kubernetes/AKS and Databricks.
• Experience in software engineering, DevOps or platform engineering environments.

Ideal Candidate Profile

The ideal candidate demonstrates a combination of:

Data Science Machine Learning Software Engineering Platform Engineering The candidate should be able to understand a machine learning or data science solution and contribute to the technical work required to build, deploy, automate, operate and maintain that solution in a production environment.

CANDIDATES TO AVOID

Candidates should generally not be prioritised where their experience is limited to:

• Pure Data Science with little or no production deployment experience.
• Pure Platform Engineering without an understanding of Machine Learning or Data Science applications.
• AI tools such as Microsoft Copilot, Power Automate or similar technologies without a strong Machine Learning Engineering foundation.
• Limited or no experience with Kubernetes, AKS or Databricks.
• Development experience without exposure to production deployment, CI/CD or DevOps.
• Repeated short-term employment periods, particularly multiple stays of approximately 6–8 months, unless there is a clear explanation for the movements.