Machine Learning Engineer

Job type: 6 months contract | potentially renewable up to 5 years

Summary:

Model maintenance, automation of ML training pipelines, and end-to-end model lifecycle management

Responsibilities:

• Design, build, and maintain robust machine learning models into AzureML.

• Develop reusable components for model training, evaluation, and deployment.

• Monitor model performance in production and implement automated retraining workflows.

• Optimize models for accuracy, latency, scalability, and cost efficiency.

• Collaborate with data scientists to productionize experimental models using best engineering practices.

• Hands-on experience on end-to-end model deployment on Azure is critical — we strongly prefer candidates with hands-on model development and maintenance experience, ideally with AzureML

• Preference for candidates have experience of develop and deploy propensity models.

Skills/Experience:

• Strong programming skills in Python and familiarity with ML frameworks (lightBoost, sklearn).

• Solid understanding of model evaluation, drift detection, and monitoring techniques.

• Experience in building and maintaining CI/CD pipelines for ML (e.g., MLflow, Kubeflow, Airflow, SageMaker Pipelines) on Azure.

• Hands-on experience with model serving using APIs or microservices (Docker, Kubernetes).

• Hands-on experience on end-to-end model deployment on Azure is critical — we strongly prefer candidates with hands-on model development and maintenance experience, ideally with AzureML

• Preference for candidates have experience of develop and deploy propensity models.