Artificial Intelligence Jobs

Hadean · London

Lead a new AI-powered defence product team, line-managing AI/ML, simulation, infrastructure and web engineers while staying hands-on in TypeScript, React and Node. You own delivery end-to-end, hiring and people development, partnering with Product and technical leads, with hybrid working one day a week in Shoreditch.

Thought Machine · London

Owns and resolves high-priority incidents across hosted SaaS and bank-hosted Vault deployments, acting as a domain expert in Vault Core. Builds tooling, dashboards and automation to improve observability and support delivery, partnering with Engineering and Product on platform stability. Leads escalations end-to-end and mentors across client, product and engineering boundaries.

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.

Matchtech · Cambridgeshire · £450 – £650 pd

Contract Python developer role focused on building and maintaining software for integration with a defence cloud environment. Day-to-day work spans data and AI, automation and tooling, and DevOps, using Red Hat OpenShift, Quay, HashiCorp Vault, Tekton Pipelines, GitHub, ArgoCD and SonarQube within agile teams. Requires UK Eyes Only clearance eligibility and is based on-site/hybrid in Cambridgeshire.

Mason Frank International · London · £70,000 – £75,000 pa

Design and deliver AI-powered Salesforce solutions for public sector and regulated organisations, building Agentforce agents, AI copilots and intelligent workflows. Development uses Apex, LWC, Flow, JavaScript, APIs and MuleSoft, integrating Salesforce with enterprise platforms under secure-by-design principles. Works alongside architects, consultants and senior stakeholders in Agile delivery teams.

Adria Solutions · Huddersfield · £35,000 – £45,000 pa

Supports AWS infrastructure, CI/CD pipelines and automation for software and machine learning workloads, including packaging, versioning and deploying ML models. Works alongside software and AI engineers to keep development, testing and production environments consistent, monitoring pipelines and fixing issues. Suited to an early-career engineer with cloud, scripting and Git foundations and an interest in MLOps.

Harnham - Data and Analytics Recruitment · London · £80,000 – £85,000 pa

Design, build and deploy scalable AI and machine learning solutions from concept to production, including Generative AI applications using LLMs and orchestration frameworks. Write production-grade Python, apply MLOps practices such as CI/CD, containerisation and monitoring, and build data pipelines for training, fine-tuning and inference. Work hybrid in London alongside data scientists, engineers and business stakeholders on large-scale datasets.

PhysicsX · New York, United States · US$150,000 – US$190,000 pa

Delivery-focused role embedding physics-aware AI models into customer tools, working on-site with clients across aerospace, automotive and energy. Involves building reliable ML data pipelines, manipulating 3D point-cloud and mesh data, and translating R&D outputs into reusable libraries and products. Expects travel of 3-4 weeks per quarter to customer sites worldwide.

Experis · London · £600 – £644 pd

Builds production-grade GenAI and Agentic AI products within a product squad, using LLMs, RAG and agentic workflows. Requires strong Python engineering plus LangGraph, vector search, evaluation and Azure AI services to deliver secure, maintainable AI applications.

Darktrace · London

Deploys, integrates and optimises AI-driven security technology across network, cloud, OT and email environments for post-sales customers. Handles deployment projects against SLOs, resolves support tickets and acts as a trusted advisor on security infrastructure design, liaising with sales, customer success and operations.

Faculty · London

Builds client-facing products that visualise insights from data scientists, working in cross-functional delivery teams within the National Security and AI Safety unit. Owns frontend standards and reusable component libraries across the company, sets up deployment infrastructure, and mentors other engineers. Requires React, TypeScript, ES6, HTML/CSS3 and data visualisation such as d3, with cloud exposure to AWS, GCP or Azure.

Speechmatics · Cambridge

Own the infrastructure behind speech AI models: building scalable data pipelines for ingesting, transforming and validating large training datasets, plus web scraping and automated data acquisition. Also operates the platform for training, evaluating and serving models, optimising GPU utilisation, scheduling and observability across distributed systems.

Faculty · London

Deliver bespoke, production-grade machine learning systems for government and national security clients, spanning the full ML lifecycle from architecture to deployment. Work with Python, Scikit-learn, TensorFlow or PyTorch, plus cloud platforms and Docker/Kubernetes, while advising stakeholders and setting deployment standards.

OpenAI · London

Builds and scales the infrastructure that trains and deploys AI agents, spanning a novel in-house container orchestration platform, FastAPI and gRPC APIs, and Terraform-managed cloud systems. Works closely with researchers on large-scale training runs and with product teams launching agentic products to hundreds of millions of users.

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.