Job Description

Job Summary
You will be at the core of designing, deploying, and maintaining robust AI/ML solutions in production, with a strong focus on pricing models, demand forecasting, route optimisation, and real-time logistics decision-making.

• Working closely with Data Science, Engineering, and Product teams, you'll ensure models are production-ready, scalable, and delivering measurable impact.
• Build and deploy ML models that power real-time, high-impact decisions.

Shape the future of pricing, shipping, and logistics with AI at global scale.

• Build and maintain scalable ML pipelines for pricing optimisation, shipping rate prediction, and logistics operations.
• Deploy, monitor, and manage ML models in production using best-practice MLOps frameworks and tools.
• Collaborate with data scientists to take prototypes through to production-grade solutions.
• Implement automated model monitoring, retraining, and version control processes to ensure performance stability.
• Optimise model latency and cost efficiency for real-time decision-making in high-volume transactional environments.
• Partner with engineering teams to streamline data flows from operational systems (ERP, WMS, TMS) and ensure data readiness.
• Champion reproducibility, scalability, and operational excellence in AI delivery.

The Successful Applicant

• Bachelor's/Master's in Computer Science, Data Science, Engineering, or related field.
• 4+ years of hands-on experience in MLOps, with a strong background in deploying and managing ML models in production.
• Proven experience in pricing optimisation, shipping, logistics, or supply chain AI solutions.
• Strong Python skills and experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn).
• Familiarity with MLOps tools and platforms (MLflow, Kubeflow, SageMaker, Vertex AI, Azure ML).
• Solid understanding of CI/CD, containerisation (Docker, Kubernetes), and cloud-based ML infrastructure.
• Experience working with APIs and integrating ML into operational workflows.

Skills Required