Top AI Jobs in Manchester

Lead the architecture and implementation of a production-grade generative AI media platform. Build reproducible, versioned pipelines, train LoRAs, integrate control techniques (ControlNet, IP-Adapter, inpainting), automate QC against product references, deploy internal tools, and partner with design to own the technical roadmap and enable creative teams.

The role involves architecting AI solutions for compliance, ensuring regulatory adherence, automating processes, and enhancing team capabilities through technology and training.

Manage multilingual data collection and Large Language Model evaluation projects, ensuring quality assurance and effective global team coordination.

Join Verve as an Insight Associate, focusing on AI-powered market research. Learn to design research, interpret human behavior, and contribute to client projects using AI tools.

Lead enterprise AI strategy and roadmap; build and scale cross-functional AI/ML teams; architect and deliver LLM, RAG, predictive and data-heavy AI products; establish MLOps, governance, cloud-native platforms (Azure/Databricks/AWS/GCP); drive product lifecycle, KPI tracking, security, and responsible AI practices.

Lead the Java Full Stack technology strategy, implementing AI-driven development practices, overseeing complex platforms, and driving modernization initiatives in cloud and microservices.

The AI & Automation Lead will oversee a team in delivering automation and AI solutions, providing technical guidance, ensuring quality, and engaging clients in various industries to support their transformation journeys.

Design, build, and productionize AI/ML solutions within the Workday practice. Collaborate with data scientists and engineers to deploy scalable models, implement MLOps (CI/CD, versioning, monitoring), integrate with enterprise data systems and cloud platforms, and ensure reliability and performance of AI-driven features.

Design and deliver enterprise AI solutions using Microsoft cloud technologies. Develop .NET software, build prototypes, QA releases, debug across environments, produce technical documentation, and support engineering process improvements within agile teams.

Lead AI Developer to capture client requirements, translate workflows into technical designs, provide technical leadership and mentoring, and contribute hands-on development in .NET/C#/Azure/React. Implement generative AI features (LLMs, RAG, Agentic AI), oversee backlog refinement, sprint planning, code reviews, testing, and support Azure, Power Platform, and M365 cloud-first delivery.

Provide technical leadership and client-facing requirements capture; translate business processes into technical designs; hands-on development in C#/.NET, Azure and React; implement generative AI features (LLMs, RAG, Agentic AI); mentor developers; lead sprint planning, backlog refinement, code reviews and QA; support Azure, Power Platform and M365 cloud-first delivery.

Lead AI transformation programmes for local government using PRINCE2 Agile and Scrum. Coordinate technical teams, manage budgets, risks, governance, stakeholder engagement, reporting, documentation, and benefits realization while acting as primary client contact and steering group representative.

Lead multiple concurrent AI transformation programmes for public sector clients, owning governance, benefits realisation, budgets, risk, stakeholder management, change and adoption, and ensuring responsible AI deployment across delivery teams.

Lead organisational change for AI implementations across public-sector clients. Design and deliver change strategies, stakeholder engagement, communications, training, readiness assessments, adoption tracking, benefits realisation, and support transition to business-as-usual alongside delivery teams.

Own product areas across an AI-driven neobank app (payments, savings, investing, insurance, travel). Define problems, roadmap, success metrics; work with engineering, design, data, operations, compliance and business teams. Launch features, monitor performance, iterate using data and user feedback. Balance speed, UX, business impact and compliance while driving execution.

Lead product direction and execution for major AI neobank app areas. Define roadmaps, priorities, metrics, and product standards across squads. Guide PMs, designers and engineers; balance strategy, UX, data, engineering and business outcomes. Work across insurance, payments, savings, investing and travel; translate regulatory and operational constraints into practical product decisions. Drive launch quality, post-launch learning, and continuous improvement while building a fast, measurement-driven product culture.

The Senior Director Analyst, MDM at Gartner will lead analysis and provide insights to help enterprise leaders succeed with their priorities in business and technology.

Lead design and implementation of high-performance agent runtimes, backend services, and self-hosted model/agent infrastructure. Build low-latency execution systems, real-time data pipelines, telemetry loops, CI/CD for zero-downtime deployments, and ensure fleet reliability on AWS/EKS.

The AI Creative Designer creates high-quality, AI-generated visuals for marketing campaigns, collaborating with teams to optimize visual content for conversion rates.

Lead budgeting, forecasting, and variance analysis while building AI-driven financial models, scenario planning, KPIs, and dashboards. Automate FP&A processes, deliver insights to senior leaders, and collaborate with cross-functional teams to improve forecast accuracy and strategic decision-making.

Design, implement, and optimize AI-driven solutions across the claims lifecycle (intake, investigation, adjudication). Build ML models for data extraction, fraud detection, reserve setting, subrogation, and adjudication decision engines. Collaborate with underwriting, fraud, legal, and IT to integrate models into operations and improve efficiency, accuracy, and compliance.

Design, implement, and optimize AI-powered solutions to support financial compliance and controls. Monitor GAAP/IFRS adherence, automate controls and reporting, support internal/external audits, and manage complex accounting areas like revenue recognition and lease accounting while collaborating with finance, audit, and technology teams.

Design, implement, and monitor AI/ML models to improve underwriting risk assessment, pricing, and portfolio management. Translate underwriting rules into data models, integrate models into workflows, analyze datasets for risk patterns, build decision-support tools, ensure compliance and fairness, and collaborate with underwriting, actuarial, product, and engineering teams.

Drive data-informed capital allocation by building financial models (NPV, IRR), applying AI/ML for forecasting and scenario planning, creating build-vs-buy and prioritization frameworks, implementing decision-support systems, collaborating with cross-functional stakeholders, and monitoring post-investment performance to refine allocation models.

Lead modernization of month-end and year-end close processes using AI and automation. Prepare, review, and validate journal entries, accruals, and financial statements. Implement anomaly detection, automated reconciliations, variance analysis, and predictive insights. Collaborate with data teams to structure finance data for ML use cases and design audit-ready, scalable reporting workflows.