Top AI Jobs in Manchester

Embed with client teams to architect, deploy, and scale enterprise AI platforms. Own time-to-value, adoption, reliability, and scalability; lead experiments, governance, integration, and executive-facing strategy while managing engineering teams and reusable deployment patterns.

Design scalable AI solution patterns, operating models, governance, and architectures for content and marketing. Assess platforms and vendors, translate prioritized AI use cases into platform requirements, solution designs, and delivery roadmaps, and define reusable standards to enable rollout across teams.

Lead design, development, and deployment of scalable Data & AI solutions on Databricks and Microsoft Foundry. Define AI technical roadmap, mentor an AI engineering squad, partner with stakeholders to prototype and scale AI products, and ensure governance, security, and ethical AI practices across enterprise platforms.

Lead and execute the enterprise AI engineering strategy: build, deploy, and operate secure, reusable AI solutions; establish engineering standards and frameworks; grow high-performing AI teams; and partner with product, architecture, data, and technology leaders to industrialize AI capabilities safely at scale.

Design, build and optimize generative AI content solutions across text, image, video and 3D. Develop prompt engineering, RAG and multimodal workflows, automated visual pipelines, and governance for brand-safe, production-ready AI content in partnership with Marketing, Creative and Product teams.

Support productionising AI/ML solutions for Workday products under senior guidance. Collaborate with data scientists and engineers to deploy, monitor, and maintain models using cloud, containerisation, and MLOps practices while following responsible AI and data privacy guidelines.

Design and deploy AI-powered content generation systems across text, image, video and 3D. Develop prompt engineering, RAG and multimodal workflows, build visual content pipelines, partner with stakeholders to productionize solutions, and implement governance, quality and responsible AI practices.

Lead finance analytics, own data warehouse strategy and governance, build a reporting Center of Excellence, implement Finance AI automation and data pipelines, optimize GCP costs, create self-service analytics, manage a small cross-functional team, and ensure data governance and audit readiness.

Manage the Onboarding Risk team across regions, ensuring high-quality risk decisions, developing team members, and owning onboarding risk procedures. Support complex escalations, collaborate cross-functionally, and keep processes compliant with card scheme rules and payments legislation while improving KPIs and decision frameworks.

Design, build, and deploy production-scale AI systems including multi-agent workflows, RAG pipelines, and fine-tuned LLMs. Deliver full-stack web and backend integrations, develop evaluation frameworks and monitoring, iterate prototypes into production, and shape internal best practices for applied AI.

Lead end-to-end delivery of AI projects for the European maritime portfolio, managing scope, schedule, budget, risk, quality, and client relationships. Coordinate teams of 5–10 technical and domain specialists across multiple concurrent engagements, enforce delivery standards, forecast resources and financials, report portfolio performance, and drive continuous improvement.

Coordinate and support end-to-end delivery of AI projects, liaising with clients and technical teams, maintaining plans/logs/reports, troubleshooting delivery issues, ensuring quality and validation, and contributing to knowledge sharing and cross-service AI initiatives.

Lead client-facing discovery and pre-sales for AI solutions, design end-to-end AI architectures and roadmaps, shape proposals and SOWs, ensure smooth handoffs to delivery, and collaborate with the AI Center of Excellence to build reusable assets and governance-aware solutions.

Lead development of a generative AI proof-of-concept and integrate AI solutions into software workflows. Mentor data engineers, design and implement Python-based generative AI applications, build scalable data pipelines, deploy models/APIs, and translate stakeholder requirements into technical solutions while staying current on AI and data engineering best practices.

Lead strategy delivery for assigned Agentic AI domains: shape use cases, assess value and feasibility, create roadmaps and value cases, drive execution with business and technology partners, manage risks and dependencies, and track measurable outcomes to ensure aligned, deliverable AI initiatives.

Define and build Fuse's inference-serving architecture and stack, including request routing, batching, scheduling, and autoscaling for latency-sensitive workloads. Own model-level optimizations (quantisation, distillation, speculative decoding), translate performance SLAs into capacity plans, and integrate low-level GPU/CUDA performance work with serving infrastructure. Set benchmarks, tooling, and act as technical owner of inference performance and reliability.

As an AI Product Manager, you will lead the application of AI in Health Information Systems, manage product portfolios, and ensure compliance with healthcare standards while delivering technology solutions that enhance patient care.

Ownership of backend features for Agentic Tools: design and implement GraphQL/REST APIs, build secure scalable Ruby on Rails services, improve RSpec automated tests, collaborate across product and AI teams, participate in Tier 2 on-call, and shape architecture for AI agent interactions with GitLab.

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.

Lead and scale an AI Security incubation team to drive technical GTM strategy, recruit and mentor principal AI security specialists, enable global sales with repeatable POVs and playbooks, advise Fortune 500 C-levels on safe AI deployments, and feed field insights into Product and Engineering to drive revenue growth.

Build and operate production-grade Kubernetes clusters and CI/CD (Harness/GitOps) for ML lifecycle. Deploy and optimise GPU-backed inference (CUDA, TensorRT, Triton, TorchServe), implement observability (Prometheus, Grafana, Dynatrace), manage model registry/MLflow, enforce auditability and reproducibility, enable progressive delivery and continuous training/monitoring, and run on‑call reliability processes with alerting and runbooks.

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

Lead field-facing AI security strategy for enterprise customers by designing secure LLM/RAG architectures, delivering demos and POVs, advising on data protection and unsanctioned AI usage, and scaling enablement and product feedback across sales, product, and engineering teams.

The Principal AI Platform Engineer leads AI architecture design and implementation, guiding teams to deliver secure AI solutions and mentoring engineers.

Senior Azure AI Engineer working in an agile AI squad to design, develop and deploy Azure-based AI solutions — including assistants, copilots, retrieval/RAG systems, cloud-native applications, integrations, security/governance, testing and optimisation — collaborating with analysts, architects and delivery teams to deliver business outcomes.