Artificial Intelligence Jobs

Faculty · London

Build and scale AI-powered systems for public sector clients, owning the full stack from Python/FastAPI backends to TypeScript and React or Vue frontends. Work in cross-functional teams with data scientists and product managers, deploying to AWS, Azure or GCP with Docker and building data pipelines and cloud infrastructure. May require UK Security Clearance and occasional on-site work with government clients.

Faculty · London

Builds and deploys production-grade ML software, tools and infrastructure, creating reusable solutions that accelerate ML delivery and capability testing of frontier AI models. Leads technical scoping and architectural decisions, advises clients and partners, and works with frontier labs on AI safety red teaming and evaluation.

Carrington Recruitment Solutions · Birmingham · £80,000 – £90,000 pa

Hands-on full stack lead role guiding a small development team across a portfolio of applications, setting engineering standards while collaborating with Product, Architecture and wider technical groups. Tech stack spans C#, .NET, Python, Azure PaaS (Functions, Logic Apps, Cosmos DB, App Service, ARM/Bicep), SharePoint and React, with an AI edge and GenAI exposure welcome. Mainly remote with occasional Birmingham office visits and light travel to other offices.

Carrington Recruitment Solutions · Sheffield · £80,000 – £90,000 pa

Hands-on full stack lead role guiding a small development team across a portfolio of applications, setting engineering standards while collaborating with Product, Architecture and wider technical groups. Work spans C#, .NET, Python, Azure PaaS, SharePoint and React, with an AI edge including GenAI tooling and taking experiments through to production-grade solutions.

PhysicsX · New York, United States · US$150,000 – US$250,000 pa

Builds and deploys customer-facing applications on top of physics AI models, working directly with clients, data scientists and ML engineers. Involves full-stack work: FastAPI REST APIs and Python microservices, plus cloud deployment with Helm, Kubernetes, Terraform and AWS/Azure. Expect on-site travel to customer sites for 3-4 weeks per quarter.

PhysicsX · Singapore

Lead technical delivery of physics-informed AI, architecting deep learning models and optimisation methods to predict and control physical systems. Own data pipelines and pre-processing best practices while mentoring a team and collaborating with simulation engineers and customers. Involves frequent on-site customer travel across multiple regions.

Anthropic · London · £150,000 – £190,000 pa

Pre-sales architect role building technical relationships with Global and Regional System Integrators and cloud partners (AWS, GCP) to accelerate indirect revenue and enable partners' AI practices. Involves joint solution development, reference architectures, unblocking strategic customer deals, and representing the company at partner events and hackathons.

Anthropic · London · £165,000 – £190,000 pa

Pre-sales architect acting as a trusted technical advisor to large enterprises, helping them integrate and deploy Claude across their technology stack. Combines deep LLM expertise with customer-facing work, from technical discovery and evals to scalable architecture design, while feeding integration insights back to Product and Engineering.

Thought Machine · Lisbon, Portugal

Full-stack role building end-to-end features across web applications and backend services, with React and TypeScript on the front end and REST APIs behind. Engineers own requirements from design and task breakdown through testing, debugging and production, working in a cross-functional team for banking clients.

Thought Machine · Lisbon, Portugal

Owns complex, ambiguous technical challenges end-to-end, shaping long-term platform architecture and company-wide engineering standards. Drives adoption of AI tooling, leads technical interviews, and mentors senior engineers while acting as a link between leadership and engineering teams. Based in the Lisbon office, requiring three days a week onsite.

Palo Alto Networks · London

Acts as "customer zero" and advocate for Identity Security, defining and validating enterprise architectures for machine identity, secrets governance and agentic identity management. Produces technical content such as blogs, whitepapers and deep-dive decks, builds reference architectures and demos, and partners with specialist sellers and solutions teams on complex customer engagements. Also feeds product insights and leads market education through executive briefings and conference talks.

Databricks · London

Lead and scale the UK Forward Deployed AI Engineering team, delivering professional services engagements that help EMEA customers productionise GenAI and ML applications. The role combines people leadership, executive customer relationships, and cross-functional work with Product, Research and Sales to shape delivery models and the product roadmap.

Wayve · London

Lead a team of ML engineers and applied scientists building synthetic data from generative world models like GAIA-3, turning them into a production engine for training-grade driving experience. Own the loop end to end, from checkpoint and large-scale GPU inference to artefacts landing in driving-model training, while setting technical direction on rig transfer, pose transfer and controllability.

Aioi Nissay Dowa Europe · Oxford · £81,322 – £112,488 pa

This role involves leading research in privacy-preserving generative AI, focusing on techniques like differential privacy, federated learning, and secure inference. The scientist will design and validate machine learning models, translate research into deployable systems, and contribute to publications and collaborations within a multi-partner environment. Emphasis is placed on responsible AI, reproducibility, and mentoring junior team members.

eFinancialCareers · London

Lead a small, high-performing team of 6-7 engineers owning core ML infrastructure, including distributed model training, LLM hosting and fine-tuning, and scalable deployment systems. The role combines hands-on technical direction with people leadership, integrating advanced ML capabilities into production-grade, high-stakes systems. Suited to experienced engineering managers, not first-time managers.