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, Dev Ops, 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 Dev Ops, 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. #J-18808-Ljbffr