Top AI Jobs in Manchester

Leads the identification, development, integration, and global deployment of AI and automation solutions for legal editorial, metadata, and content operations. The role supports AI strategy, evaluates designs against governance and quality standards, manages cross-functional projects, reports data-driven outcomes, tracks industry trends, documents improvements, and promotes responsible AI use.

Design and build scalable Azure platform capabilities for AI (Agents, RAG, LLMs), create reusable platform components (APIs, gateways, model-serving), produce high-quality technical designs, optimize performance/cost, and collaborate across teams to enable AI at scale.

Design and build scalable Azure-based AI platform capabilities (APIs, integrations, model serving) to support Agents, RAG, and LLMs. Produce high-quality technical designs, develop reusable platform components (AI gateways, orchestration services), optimize performance, cost and scalability, and collaborate with architecture, engineering, and product teams to enable enterprise AI adoption.

Designs and maintains Azure DevOps CI/CD pipelines for 5G applications across lab and production environments. Builds AI-assisted failure analysis, self-healing workflows, MCP servers, and integrations with Azure, Kubernetes, repositories, and deployments. Develops reusable YAML templates, secure automation, observability, and infrastructure-as-code solutions. Troubleshoots Kubernetes deployments, leads technical discussions and training, documents standards, and advises teams on enterprise DevOps and emerging GenAI capabilities.

Lead the technical strategy, architecture, development, deployment, and governance of AI solutions across the organisation. Design Generative AI, machine learning, LLM, and RAG platforms; establish engineering standards, MLOps, CI/CD, security, monitoring, and responsible AI practices; oversee pilots and production implementations; and mentor technical teams. Engage senior stakeholders to translate business needs into scalable AI solutions and present roadmaps, recommendations, and governance decisions.

Design, develop, and productionize AI/ML solutions within the Workday practice. Integrate models into production, implement MLOps (CI/CD, versioning, monitoring), use cloud AI services and container orchestration, build data pipelines, and collaborate with cross-functional teams to ensure scalable, reliable AI-driven features.

Design and deliver end-to-end features for DAP onboarding and repository/background flows. Build autonomous and scheduled agents that act on customer codebases, improve onboarding experience, collaborate on shared interfaces, review code and designs, and contribute to an async-first, distributed team's technical quality and ownership culture.

Design, build, and operate the high-availability event and trigger system that links GitLab and third-party events to AI-powered Duo Flows. Work in GitLab's Rails monolith, scale for high throughput, influence architecture, add trigger types, collaborate cross-functionally, and participate in Tier 2/3 on-call rotations to troubleshoot production issues.

Own the strategy, roadmap, commercial performance, client experience, operational readiness, and continuous improvement of Cloud Telephony, AI Receptionist, and CCE products. The role coordinates technology, sales, customer success, technical production, partners, and product operations to improve adoption, retention, margins, service quality, and launch execution.

Senior AWS consulting leader advising enterprise clients on cloud, data and AI strategy. Lead discovery workshops, shape large-scale transformation programmes, design cloud-native roadmaps, support pre-sales, mentor architects, represent the firm externally, and ensure strategic intent translates into delivery.

Sell an AI-powered recruitment platform: prospect and engage clients, present product features, analyze territory and customers, meet sales targets, collaborate with marketing, and provide regular performance reports while maintaining client relationships.

Design, build, and operate production-grade agentic AI systems on Amazon Bedrock. Implement multi-agent orchestration, MCP servers, RAG pipelines, vector stores, and integrations. Use AWS serverless (Lambda, DynamoDB, S3), instrument observability (CloudWatch/tracing), and collaborate with client teams to deliver scalable AI agent solutions.

Design and develop AI solutions for complex problems, integrating AI capabilities into systems and improving effectiveness through experimentation and evaluation.

Lead the adoption of AI-native software development practices as a hands-on Principal Engineer. Mentor engineers in tools such as GitHub Copilot, Claude Code, Codex, Cline, Kiro, and Cursor; introduce innovative workflows; solve complex technical problems; and collaborate with engineering and product leaders to translate AI-driven productivity and capabilities into product value. The role requires strong backend development skills, technical leadership, communication, and practical experience with generative AI tools.

Senior Software Engineer responsible for advancing AI-assisted software development across engineering teams. The role combines hands-on backend coding with mentoring, code reviews, technical leadership, workflow innovation, and adoption of tools such as GitHub Copilot, Claude Code, Codex, Cline, Kiro, and Cursor. The engineer will demonstrate AI-powered development practices, collaborate with technical and product stakeholders, and communicate productivity and product-value improvements.

Leads the design and technical direction of agentic AI support experiences for Ticketmaster. Defines reusable standards for prompts, NLU, context management, retrieval, tool use, guardrails, evaluation, and human handoff. Translates business workflows into AI behaviors and autonomous processes, monitors quality and business outcomes, reviews designs, manages risks, mentors AI engineers, and collaborates across product, operations, data science, platform, security, QA, and knowledge teams.

Own outbound sales for a PropTech AI product: cold outreach, qualify leads, book discovery calls and demos, manage CRM activity, refine sales playbook, and scale into a full-cycle closing role.

Own an AI investing product vertical end to end, including strategy, metrics, roadmap, technical design, delivery, and launch. Partner with engineering, compliance, finance, treasury, operations, and design to build reliable, auditable financial products. Shape APIs, data flows, ledgering, reconciliation, security, scalability, and failure handling while incorporating KYC, monitoring, limits, privacy, and licensing requirements. Apply AI responsibly through evaluation, safeguards, human oversight, and clear failure handling.

Owns an AI stockbroking product vertical end to end, including strategy, roadmap, technical design, delivery, regulatory requirements, AI evaluation, reliability, and launch. Partners with engineering, design, compliance, operations, treasury, and finance to build trusted financial products involving payments, trading, financial rails, and real-money transactions.

Own an AI neobank product vertical end-to-end, including strategy, roadmap, technical design, delivery, metrics, and launch. Partner with engineering on APIs, data flows, ledgering, reliability, security, and scalability. Integrate regulatory requirements across KYC, transaction monitoring, privacy, and licensing. Apply AI with evaluation, safeguards, human oversight, and failure handling while coordinating engineering, design, compliance, operations, treasury, and finance.

Own a financial product vertical end to end, including strategy, metrics, roadmap, technical design, delivery, and launch. Partner with engineering on APIs, data flows, ledgering, reconciliation, security, scalability, and failure handling. Integrate regulatory requirements across KYC, monitoring, limits, privacy, and licensing. Apply AI and LLMs with evaluation, safeguards, oversight, and reliability controls while aligning compliance, operations, treasury, finance, design, and engineering.

Own an AI stockbroking product vertical end-to-end, including strategy, metrics, roadmap, technical design, delivery, and launch. Partner with engineering on APIs, data flows, ledgering, security, scalability, and failure handling. Integrate KYC, transaction monitoring, privacy, licensing, and regulatory requirements. Apply AI with evaluation, safeguards, and human oversight while coordinating Engineering, Design, Compliance, Operations, Treasury, and Finance.

Own an AI investing product vertical end-to-end, including strategy, metrics, roadmap, technical design, delivery, and launch. Partner with Engineering, Compliance, Operations, Treasury, Finance, and Design to build reliable, auditable financial products. Manage APIs, data flows, ledgering, reconciliation, security, scalability, regulatory requirements, third-party dependencies, and AI safeguards. Drive trade-offs across correctness, speed, cost, reliability, compliance, and user experience.

Develop and test Generative AI and LLM applications in Python, including RAG systems, prompt workflows, AI agents, and API integrations. Evaluate model performance, reliability, accuracy, and responsible AI considerations while collaborating with engineering, data science, and product teams. Contribute to documentation and Git-based development, learn emerging AI technologies, and help deliver production-ready solutions.

Build and lead an AI-native consumer investing business, defining its proposition, roadmap, business model, and investment experiences. Own P&L, assets, customers, engagement, revenue, and unit economics while partnering with product, engineering, and AI teams. Develop AI-powered investment research, discovery, portfolio management, and decision support. Build investing, technology, product, and commercial teams, then expand into new investment products and markets.