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. #J-18808-Ljbffr