Top AI Jobs in Manchester

Develop end-to-end AI product features by integrating LLMs and external tools, ensuring system reliability and performance across multiple tasks.

Design and ship production agents and reusable skills on ClickStack. Own the agent stack (context engineering, tools, MCP servers), integrate with ClickHouse telemetry, build SDKs and observability, optimize latency/cost, run evals, and collaborate with open-source contributors and customers.

Own and deliver the platform's intelligence layer end-to-end: design, build, and improve agent systems for enterprise transformation. Act as the technical authority, implement production-grade LLM-based services, ground agents in real context, collaborate with Product Owner, and drive systems engineering and shipping decisions.

Design and operate large-scale GPU infrastructure for distributed AI training, ensuring reliability, performance, and efficient customer partnerships.

This job is for candidates with AI infrastructure experience to submit their resumes for future opportunities. No specific role is posted.

The Site Reliability Engineer will provision and manage Kubernetes clusters, build automation tools, debug customer issues, and improve infrastructure reliability.

Work as a consultant combining human insight, strategy and AI to deliver client insight programmes. Synthesize diverse evidence, apply methodology and emerging technologies, support proposals and business growth, and develop consultancy and analytics skills across sectors.

Lead and deliver AI-enabled insight programs, manage client accounts, combine human, cultural and artificial intelligence methods, grow client relationships and revenue, coach and develop teams, shape propositions and ways of working to evolve insight consultancy.

Support full-cycle recruiting for product, engineering, and business roles: source and screen candidates, coordinate interviews, maintain ATS data, assist offers, and run recruiting projects and process improvements.

Build and operate backend systems and orchestration layers for LLM-powered insurance workflows. Implement inference pipelines, optimize latency/throughput/cost, ensure observability, debug distributed production issues, and collaborate across product, ML, and operations to deliver reliable, scalable AI services.

Design, build, and optimize LLM-powered product features integrated into insurance workflows. Develop prompts, reasoning flows, guardrails, and validation layers; evaluate model performance with experiments and user feedback; collaborate with backend, product, and design to ship reliable, production-ready AI automation and decision-support features.

The AI Engineer designs, develops, and deploys AI-driven applications for non-data scientists, enhancing decision-making and model accessibility for organizations.

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, 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.

The Senior AI Compute Infrastructure Engineer will manage and optimize GPU and accelerator clusters for AI workloads, collaborate with teams, and ensure reliable performance and cost efficiency.

Manage curriculum development for data science and engineering, collaborate with instructors, create content, and drive improvements based on learner feedback.

As a Curriculum Manager, you will manage content development for the data science and engineering curriculum, collaborating with instructors and teams to enhance learning experiences.

The Lead Engineer (AI Native) will work closely with clients, develop AI solutions, and enhance software delivery practices while mentoring teams.

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.

The role involves providing technical leadership for AI and automation, designing scalable solutions, and ensuring project delivery aligns with business goals and enterprise standards.

Design, prototype, and deliver generative AI solutions (RAG, agents) integrating LLMs with APIs, data sources, and production systems. Build cloud-native deployments, apply LLMOps and model observability, collaborate across product and design teams, and iterate on agentic architectures using modern tooling and DevSecOps practices.

The role requires creating insights for finance leaders focused on AI integration in finance processes, conducting research, and advising on AI strategies. It involves engaging with clients, presenting insights, and mentoring junior team members.

Senior individual contributor researching how threat actors weaponize AI across the attack lifecycle. Instrument telemetry, analyze global data and OSINT, develop detections, automate research workflows with LLMs and scripting, and publish external threat intelligence to inform protections and operations.

This role involves managing revenue operations, building analytical tools, redesigning processes, and using AI to optimize workflows while collaborating with cross-functional teams.

Lead AI-focused engineering management, improving developer productivity through AI-integrated workflows, modernizing CI/CD pipelines, and ensuring alignment across teams.