Senior Machine Learning Engineer

Role Overview

We are looking for an experiencedSenior Machine Learning Engineerto design, build, deploy and support enterprise-scaleMachine Learning, Artificial Intelligence and Generative AI solutions.

The role is strongly focused on theproductionisation and operationalisation of AI and ML solutions, including machine learning models, GenAI applications, AI agents, Retrieval-Augmented Generation (RAG) solutions and reusable ML platforms.

The successful candidate will work extensively acrossDatabricks, Azure Kubernetes Service (AKS), Kubernetes, Docker, MLflow and MLOps environments, partnering closely with Data Scientists, Cloud Engineers, Platform Teams, Security Teams and business stakeholders.

This is an engineering-focused role requiring strong experience taking AI and Machine Learning solutions fromprototype through deployment, scaling, monitoring and production support.

Key Responsibilities

• Design, build, deploy and supportproduction-grade Machine Learning and AI solutions.

• Productionise Machine Learning models and Data Science pipelines usingDatabricks.

• Develop and deployGenerative AI applications, AI agents and RAG solutions.

• Build reusable Machine Learning pipelines and frameworks usingMLOps principles.

• ImplementCI/CD, automated testing, model monitoring, governance and deployment automation.

• Deploy and optimiseopen-source Machine Learning and Large Language ModelswithinAzure Kubernetes Service (AKS).

• Develop and supportREST APIs and microservicesthat expose AI and Machine Learning capabilities to enterprise applications.

• Build scalable containerised solutions usingDocker and Kubernetes.

• Implement and maintainDatabricks Workflows, MLflow, Model Serving and Mosaic AIsolutions.

• Monitor models forperformance degradation, model drift, reliability and operational health.

• Troubleshoot production issues across models, ML pipelines, APIs, GenAI applications and supporting infrastructure.

• Optimise AI and ML platforms forperformance, scalability, reliability and cost efficiency.

• Collaborate with Data Scientists to convert models and prototypes intobusiness-ready production solutions.

• Partner with Cloud, Infrastructure, Security and Platform Engineering teams to ensure solutions comply with enterprise architecture and security standards.

• Contribute to reusable engineering frameworks, standards and best practices across the AI and Machine Learning ecosystem.

• Mentor junior engineers and support knowledge sharing within the engineering team.

Core Technical Requirements

Candidates should have strong hands-on experience in:

• Python

• SQL

• Databricks

• Databricks Workflows

• MLflow

• Databricks Model Serving

• Mosaic AI

• Microsoft Azure

• Azure Kubernetes Service (AKS)

• Kubernetes

• Docker

• REST API development

• Microservices

• CI/CD

• MLOps

• Machine Learning

• Generative AI

• Large Language Models (LLMs)

• Retrieval-Augmented Generation (RAG)

• AI Agents

• Spark / distributed computing

• Cloud-native AI/ML platforms

• Monitoring and observability

• Infrastructure automation

Required Experience

The ideal candidate will have:

• Strong experience building, deploying and supportingMachine Learning solutions in production environments.

• Proven experience withMLOps, DevOps and software engineering practiceswithin AI/ML environments.

• Strong development experience usingPython, together with SQL and API development.

• Hands-on experience withDatabricks, MLflow and Model Serving.

• Strong experience withKubernetes, Docker and containerised application deployment.

• Experience deploying Machine Learning and/or Generative AI workloads ontoAzure Kubernetes Service (AKS).

• Experience implementingCI/CD pipelines, infrastructure automation and production monitoring.

• Experience withSpark or other distributed / large-scale data processing technologies.

• Practical understandin