Senior Data Scientist (ML Engineer)

What you will be doing:

• Design, prototype, build, deploy, and support production-grade Machine Learning, AI, and Generative AI solutions.


• Productionise and monitor Machine Learning models and pipelines using Databricks, including MLflow and Model Serving.


• Develop and deploy AI agents, Generative AI solutions, and Retrieval-Augmented Generation (RAG) applications on Databricks.


• Build reusable Machine Learning pipelines incorporating MLOps, CI/CD, automated testing, monitoring, and governance.


• Deploy and operate open-source AI/ML models on Azure Kubernetes Service (AKS).


• Develop scalable APIs and microservices for AI/ML solutions using Docker and Kubernetes.


• Monitor model performance, drift, reliability, and overall operational health.


• Partner with Data Scientists to productionise models and translate data science solutions into reliable production services.


• Collaborate with platform engineering, cloud, security, and infrastructure teams to deliver scalable and secure AI/ML solutions.


• Troubleshoot and resolve technical issues across Machine Learning models, pipelines, platforms, and services.


• Optimise solutions for performance, scalability, cost, reliability, and operational efficiency.


• Establish and promote engineering standards, frameworks, and best practices for AI/ML development and deployment.


• Provide technical guidance, coaching, and mentoring to other team members.


• Stay current with emerging Machine Learning, AI, Generative AI, MLOps, and cloud technologies.


• Contribute to the end-to-end delivery of AI/ML solutions, from experimentation and development through to production and ongoing support.

What we are looking for:

• A relevant Degree in Computer Science, Engineering, Econometrics, Mathematical Statistics, Actuarial Science, Data Science, or a related field.


• Minimum 5 years’ experience in Machine Learning Engineering, AI Engineering, Data Science, or a closely related field.


• Strong hands-on experience developing, deploying, and supporting Machine Learning and AI solutions in production environments.


• Experience with MLOps, DevOps, software engineering practices, CI/CD, and infrastructure automation.


• Strong programming skills in Python and SQL, with experience developing and consuming REST APIs.


• Hands-on experience with Databricks, MLflow, Model Serving, and cloud-based AI/ML platforms.


• Experience with Azure Kubernetes Service (AKS), Kubernetes, and Docker.


• Strong understanding of Machine Learning model deployment, monitoring, model drift, observability, and production operations.


• Experience with Apache Spark and distributed computing.


• Practical experience with Large Language Models (LLMs), Generative AI, RAG, and AI agents/agentic AI.


• Experience working closely with Data Scientists to operationalise and productionise Machine Learning models.


• Ability to design scalable, reliable, secure, and maintainable AI/ML solutions.


• Strong problem-solving skills and the ability to troubleshoot complex technical issues.


• Ability to communicate complex technical concepts effectively to both technical and business stakeholders.


• Experience within financial services, banking, or another regulated environment would be advantageous.

Preferred Certifications:

• Microsoft Azure certifications such as AZ-104, AZ-305, or AI-102.


• Databricks certifications in Data Engineering, Machine Learning Engineering, or Generative AI Engineering.


• CKA or CKAD Kubernetes certifications.


• Relevant DevOps, MLOps, or Platform Engineering certifications.


• Recognised Machine Learning, Artificial Intelligence, or Data Science certifications.


• AWS or Google Cloud certifications would be advantageous.

Most of our positions are remote; however, candidates should reside within travelling distance of the client site, as the working arrangements may change depending on business requirements.