Top AI Engineer Jobs in London

As an AI Research Engineer focused on AI Safety, you will develop techniques for evaluating AI products, enhance decision-making via uncertainty quantification, and ensure robustness against adversarial attacks.

The Senior AI Engineer will design and implement AI systems, focusing on production-grade solutions using LLMs and agentic systems to meet business needs.

Operate and maintain IRC's enterprise AI platforms (Anthropic, OpenAI, Azure OpenAI); manage API keys, access, configurations, and observability. Build and troubleshoot integrations, scripts, and automations (Python/JavaScript/TypeScript). Monitor usage, optimize resources, and liaise with InfoSec on security and compliance. Document runbooks, support deployments with engineering teams, and provide technical input for AI rollouts and pipeline design.

Design and implement large-scale AI/ML systems using LLMs and RAG. Develop data pipelines, optimize models, and collaborate with teams on actionable intelligence solutions.

Design and implement AI infrastructure for cross-border e-commerce: optimize GPU scheduling on Kubernetes or Ray, improve cluster high-availability and fault tolerance, build efficiency evaluation models, and introduce cutting-edge AI infra technologies.

Build and ship AI-native systems for a fintech neobank: AI agents, RAG/LLM apps, tool-calling and automations to improve onboarding, KYC, support, risk review, CRM and operations, focusing on reliability, latency, cost, security and compliance while collaborating with product and engineering teams.

Design, build, and operate enterprise-scale identity and access systems. Architect unified authorization (SpiceDB/ReBAC) and authentication (Curity/Keycloak) across platforms, implement OAuth/OIDC/SAML, build SDKs/APIs, operate SpiceDB on Postgres, and define IAM patterns, observability, and golden paths for product teams.

Embed with product and customer engineering teams to design, build, and ship production AI-powered platform solutions. Remove platform friction, implement event-driven architectures, build tooling and reference implementations for IDP adoption, and iterate on production-grade systems with monitoring and customer-facing delivery.

Build, operate and support the Internal Developer Platform (IDP) end-to-end: implement self-service workflows, scaffolders, service catalogue entries and CI/CD integration; integrate IDP with Kairos and Nexus; create developer documentation and enablement; resolve platform reliability and performance issues; contribute to platform standards and roadmap; provide platform support and enablement for product teams.

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, build, and deploy AI systems (predictive models, NLP, generative AI) from PoC to production. Work with clients to define use cases, prepare large datasets, implement deployment and monitoring pipelines using cloud-native tools, and collaborate with cross-functional teams to operationalize AI solutions.

Embed with client teams to map and redesign creative workflows, build and deploy AI-powered pipelines integrated with DAMs/CMS and martech. Lead prompt engineering, agent workflows, automation, governance, training, and metrics tracking. Deliver proofs of concept and production-ready solutions, enable adoption, and develop reusable accelerators and practice methodologies.

Embed with client teams to diagnose workflow friction, redesign operating models, and deliver AI-powered proofs of concept. Map current-state processes, build working LLM-based prototypes using low-code/no-code tools, drive adoption through change management, measure impact, and support rollout to realize business value.

Own and productionize an internal AI agent platform: build agent workflows, frontend UIs, and Python/FastAPI backends; implement observability and evaluation (Ragas, Langfuse); integrate cross-cloud systems; mentor engineers and drive durable, testable, regulated fintech automation.

The Generative AI Engineer will design and implement AI solutions, focusing on model fine-tuning, prompt engineering, and secure deployment, while collaborating with cybersecurity specialists.

The role involves designing, developing, and supporting cloud and AI solutions primarily on Azure, including automation and integration components, while ensuring security and compliance.

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.

Build end-to-end AI-first product features, design scalable APIs and frontends with React/TypeScript, integrate LLMs and AI agents, collaborate with founders and customers, and establish engineering culture and infrastructure.

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, build, and deploy Generative AI-powered applications and agents within an agile team. Work across the SDLC on back-end microservices, front-end interfaces, LLM integration, RAG pipelines, MLOps, containerized deployments, and monitoring. Contribute to GenAI strategy, model optimization, prompt engineering, and enterprise CI/CD while adhering to responsible AI and operational standards.

Responsible for deploying, operating, and ensuring reliability of a centralized AI-SDLC platform. Build/manage infrastructure, CI/CD pipelines, observability, security/compliance controls, and provide operational/on-call support to meet enterprise SLAs while optimizing cost and scalability.

Lead technical pre-sales for enterprise AI: engage C-suite, design secure multi-cloud AI architectures, run workshops, build POCs, present demos, influence commercial strategy, and ensure smooth handover to delivery.

As an ML Engineer Intern, you will build and deploy ML models, design scalable infrastructure, and collaborate with data science teams in a fast-paced environment.

As an ML Engineer, you'll build and deploy scalable ML systems, optimize models, and collaborate with teams to improve content across platforms.

The Founding Product Engineer will develop AI-integrated platforms for healthcare, improving administrative workflows and enhancing the patient-clinician experience within the NHS.