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.