Artificial Intelligence Jobs

PhysicsX · Singapore

Join a new Singapore research team building physical foundation models for industrial engineering, translating physics challenges into mathematical formulations and training deep learning models on large datasets. You will own research workstreams, run experiments, and communicate results to customers and at publication venues, with a focus on operator learning, geometric deep learning, or generative models for geometry and spatiotemporal data.

PhysicsX · Singapore

Builds physical foundation models for industrial engineering, scaling training across multi-GPU cloud compute and turning research prototypes into robust, optimised implementations. Works closely with research scientists, simulation engineers and customers, owning research work-streams and translating results into reusable libraries, tooling and products.

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 machine learning systems for national security and AI safety clients, including LLM applications, multi-agent tooling and evaluation of frontier models. Involves technical scoping, architectural decisions, and advising stakeholders, with reusable infrastructure work alongside engineers and data scientists.

Darktrace · Cambridge

Owns the Network Detection and Response product roadmap end to end, deciding what gets built and in what order. Works directly with customers running zero-trust, cloud-connected and segmented estates, partners with R&D on prioritisation and testing, and supports Product Marketing on launches and go-to-market. Acts as internal subject matter expert for the NDR portfolio.

Darktrace · London

Owns the end-to-end roadmap for Darktrace's Network Detection and Response product, deciding what gets built and in what order. Works directly with customers running complex zero-trust, cloud-connected and segmented estates, partners with R&D on prioritisation and testing, and supports Product Marketing on launches and go-to-market. Acts as the internal subject matter expert for the NDR portfolio.

Hays Technology · London · £100,000 – £110,000 pa

Hands-on role building the backend services, APIs and data pipelines behind a new AI layer for an investment management firm's portfolio platform. Involves LLM applications such as agent workflows, tool use and RAG, plus security controls, automated tests, AI evaluations and monitoring of accuracy, latency and cost in production.

Wayve · London

Builds Python services and microservices that orchestrate the end-to-end model-release workflow, integrating feature candidates into release branches and automating readiness checks before training. Connects training platforms, simulation and on-road testing, and productionises services on Azure and Kubernetes with observability and approval gates.

Interface Recruitment · Leeds

Lead a team of four to own the innovation agenda at an FCA-regulated asset manager, translating technology strategy into actionable roadmaps and championing AI, machine learning, generative AI and intelligent automation. Oversee innovation governance, value realisation and risk management, partnering with business, risk and technology teams to strengthen operational resilience within regulatory expectations.

Wayve · London

You'll design, build and operate shared infrastructure for model access, agent runtimes and observability, taking projects from problem definition through technical design, deployment and ongoing operation. Working with security, IT and engineering teams, you'll create governed access layers, reusable tooling and standards so teams can run AI agents in production without one-off infrastructure.

Medtronic · London · £95,200 – £142,800 pa

Builds and maintains the internal platform tooling that other engineering teams rely on, spanning Infrastructure as Code with OpenTofu, EKS and Helm, CI/CD automation, and observability with OpenTelemetry. Works across software groups to remove developer productivity gaps and supports AI agentic workflows, with technical mentorship expected. Based in London with two to three days a week in the office.

83zero · London · £75,000 – £90,000 pa

Design and build intelligent Salesforce solutions, integrating AI capabilities such as Einstein, Prompt Builder and Agentforce into platforms and workflows. Work with business, product and engineering teams to translate complex requirements into scalable technical solutions, optimising data models, automation and integrations with enterprise systems using Apex, LWC and APIs.

Scale AI · London

Builds and scales backend systems and APIs that bring large-scale generative AI products to production for enterprise customers. Work spans distributed data systems, model serving, deployment pipelines, and cloud infrastructure across AWS, Azure, or GCP, in close collaboration with ML and product teams. The role centres on reliability, latency, throughput and cost optimisation in hybrid and multi-cloud environments.

Medtronic · London · £76,800 – £115,200 pa

Develops and maintains core application software for intra-operative medical devices, primarily in modern C++ (C++17+), within a multidisciplinary team spanning embedded, cloud and application software. Work covers device monitoring, cloud connectivity, debugging, and security and regulatory compliance for medical-grade systems, with two days per week onsite in London.

VIQU IT · Cornhill, Greater London · £90,000 – £95,000 pa

Owns the end-to-end architecture of an enterprise AI platform spanning Azure and AWS, covering ML training, model hosting, MLOps and Generative AI. Defines patterns for LLMs, RAG, vector search and agent frameworks, evaluates tooling, and provides technical direction to ML and platform engineers while embedding security, governance and regulatory requirements.