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# Lead Machine Learning, AI Engineer

- **Company:** Opportunitiescomply
- **Location:** Manchester, UK
- **Experience:** Expert
- **Contract:** Permanent contract
- **Skills:** Artificial Intelligence, Data Analysis, Microsoft Azure, Cloud Computing, Continuous Integration, Information Engineering, DevOps, Python (Programming Language), Machine Learning, Azure Machine Learning, Software Engineering, Management of Software Versions, Azure Service Bus, Pytorch, Machine Learning Operations, Azure Synapse Analytics, Stream Analytics, Data Pipelines, Web Api
- **Published:** September 7, 2026
- **Apply:** https://www.apply4u.co.uk/jobs/lead-machine-learning-ai-engineer/46386530

## About the Role

issuesConduct model testing, validation and test automationDeploy ML and AI models as API endpoints for internal and partner systemsLead system design and architecture discussions and share knowledgeLiaise with senior stakeholders to improve strategic business decisions and identify opportunitiesComply with the Group Code of Conduct, Fitness and Propriety policies, company policies, values, guidelines and relevant regulationsRequirementsExtensive experience building end-to-end systems as a Platform Engineer, ML DevOps Engineer, or Data EngineerExperience building integrations between cloud-based systems using APIsExperience developing and maintaining ML and AI systemsExperience with agile ways of working in a data science, machine learning and AI environmentExperience designing or maintaining data software development lifecycles and continuous integration and deployment (CI/CD)Exposure to machine learning and AI methodology and best practicesCoaching experienceBachelor's or master's
degree and/or equivalent professional experienceDeep experience and strong understanding of Microsoft Azure, including Azure ML, Azure Stream Analytics, Cognitive Services, Event Hubs, Synapse, and Data FactoryFluency in Python and modelling frameworks such as PyTorch and TensorFlowSkilled in applying MLOps frameworks within a production environmentExcellent verbal and written communication skillsStrong time management and organisational skillsAbility to diagnose and troubleshoot problems quicklyExcellent problem-solving and analytical skillsStrong stakeholder management and line-management abilityAbility to work independently and as part of a teamCore CompetenciesDemonstrates expertise in implementing and improving machine learning and AI operations frameworks, with a strong focus on cloud AI Ops using Microsoft Azure. Proficient in developing production-ready ML and AI systems, ensuring model auditability, and optimizing deployment processes.Highest-signal resume keywordsMachine
Learning Operations (MLOps)Microsoft AzurePython ProgrammingCI/CD PipelinesCoaching and Team LeadershipHard SkillsMachine LearningAI Systems DevelopmentData Pipeline EngineeringModel Testing and ValidationAPI DevelopmentData Software Development LifecycleModel VersioningAutomated MonitoringData TransformationModel DeploymentSoft SkillsExcellent Communication SkillsStrong Problem-Solving SkillsTime ManagementOrganizational SkillsStakeholder ManagementCertifications & QualificationsBachelor's DegreeMaster's DegreeIndustry KeywordsMachine Learning MethodologyAI Best PracticesAgile DevelopmentData EngineeringProduction EnvironmentTools & TechnologiesAzure MLAzure Stream AnalyticsCognitive ServicesEvent HubsSynapseData FactoryPyTorchTensorFlow #J-18808-Ljbffr

## Description

Implement and continuously improve machine learning and AI operations frameworksDesign and deliver tools, framework components and engineering practices for production-ready ML and AI systemsCoach and guide a small team of MLOps Engineers, Analytics Engineers and AI EngineersCollaborate with Data Science, Pricing, Data Engineering and Software Development teamsEvolve Machine Learning and AI Engineering standards and frameworksEnhance data pipelines and engineering infrastructure for scaled ML and AI solutionsSupport data acquisition, transformation, model discovery and developmentEnsure model auditability, versioning and data securityDesign and implement cloud AI Ops using AzureOptimise deployment of ML and AI model scoring code in production servicesUse CI/CD pipelines and manage deployment and versioning of data science and AI modelsDevelop automated monitoring for model execution, quality, degradation and operational performanceManage remediation of priority 2, 3 and 4 production


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