Top AI Jobs in London

Develop and benchmark 3D computer vision and localisation systems (SLAM, visual odometry, scene matching, geo-registration, 3D reconstruction). Extend geometric deep learning and feature-matching methods, design experiments and benchmarks, adapt models to hardware constraints, and collaborate with research, engineering, and product teams to deploy robust AI solutions.

Lead Responsible AI, ethics and assurance engagements: design frameworks, perform impact and ethical risk assessments, define assurance approaches, advise on AI governance, review AI systems (including GenAI/LLMs), support bids, and build internal capability through playbooks, training and delivery assurance.

Lead end-to-end implementation of AI tools for Macro & Fixed Income PMs: build prompts, datasets, vector stores, and pipelines; deliver training and playbooks; drive adoption and measure ROI; liaise with central AI/Tech and Compliance to ensure governance and production-grade deployments.

Develop and maintain front-end and back-end AI-enabled web and desktop applications. Collaborate with cross-functional teams to design features, optimize scalability and security, troubleshoot defects, participate in code reviews, and implement AI assistant technology integrated with enterprise systems.

Design and build Python services, pipelines, testing frameworks, data analysis tools, and lifecycle management systems for quantitative market and credit risk models. Implement auditable AI/ML and LLM-based automation for documentation, data quality, testing, and workflow assistance. Collaborate with quants, validators, data engineers, and program leaders while promoting secure engineering practices, mentoring developers, and contributing to technical design.

Design, build, evaluate, and deploy AI systems for enterprise customers. Responsibilities include creating prototypes and production integrations, diagnosing implementation issues, making decisions about reliability, security, governance, latency, cost, and safety, and helping customers scale adoption. The role also develops reusable architectures, tooling, playbooks, and technical guidance while partnering with customer and internal engineering, product, research, security, and go-to-market teams.

Design, build, deploy, and support agentic AI and intelligent automation solutions across enterprise platforms. The role develops autonomous workflows, orchestration, RAG pipelines, prompts, integrations, guardrails, human oversight, monitoring, and auditability. It also establishes AI engineering standards and supports production reliability, incident resolution, governance, and continuous improvement while communicating technical designs and risks to diverse stakeholders.

Build practical AI-powered applications, internal tools, workflow automations, and platform features. Integrate LLMs, APIs, services, databases, and business data across cloud-based systems. Develop, test, monitor, debug, document, and improve production-ready software while collaborating with engineering, product, operations, and business stakeholders. Apply prompt engineering, retrieval workflows, and responsible AI practices to solve operational problems.

Leads the technical direction, quality, and growth of an AI Engineering practice. Responsibilities include designing production AI architectures, overseeing major client engagements, setting engineering standards, leading technical workstreams, conducting design reviews, shaping proposals, advising executives, developing senior engineers, and defining hiring and capability plans. The role remains hands-on with architecture, code reviews, prototyping, and resolving complex technical issues across retrieval, agentic, evaluation, multilingual, scalability, cost, and reliability challenges.

Leads end-to-end technology strategy and architecture for strategic ISVs and SaaS companies across AI, cloud modernization, and developer productivity. Drives Microsoft Foundry, GitHub Copilot, Agentic SDLC, Azure adoption, cloud-native architectures, workshops, hackathons, proof-of-concepts, and executive engagements. Serves as the customer’s technical advocate within Microsoft while influencing engineering teams, accelerating Azure consumption, and guiding intelligent application development.

Own MuleSoft adoption, consumption, business outcomes, growth, health, and retention across strategic customer accounts. Build prototypes and proofs of concept, lead product demonstrations, analyze consumption data, identify governance and orchestration gaps, and guide customers toward value realization. Partner with sales leaders, architects, CTOs, and platform teams to connect integration, data, AI agents, and ROI. Develop expertise in Agent Fabric and share best practices across a global team.

Owns deployment, operations, reliability, and productionization of a centralized enterprise AI-SDLC platform. Responsibilities include infrastructure management, CI/CD automation, monitoring, observability, incident response, security and governance controls, multi-tenant scalability, cost management, and operational support. The role uses cloud, containers, infrastructure as code, scripting, and reliability engineering practices, with preferred experience in LLMOps, MLOps, model hosting, API gateways, and AI platform management.

Own technical delivery of Pigment’s solver pilot program and build production-grade optimization capabilities. Formulate supply chain optimization models, configure customer pilots, develop Python models with third-party solvers, benchmark results, support implementations, and collaborate with Engineering, Data Science, and solver vendors on production integration.

Design and build production agentic AI systems for shopper experiences, platform automation, and internal efficiency. Own agentic platform architecture, LLM evaluation strategies, context management, deployment, scaling, monitoring, latency, quality, explainability, cost, and token usage. Establish company-wide agent development practices and enable technical and non-technical teams to use AI tools safely. Collaborate with product, design, mobile, and machine-learning teams in an office-based London startup environment.

Design, build, and maintain automated test suites, evaluation scenarios, metrics, and datasets for AI-powered features. Automate QA workflows with Python and CI/CD, test APIs and ML model outputs, define quality strategy and acceptance criteria, create CI/CD quality gates, mentor teams, participate in code reviews, and document findings.

Own product areas end-to-end, designing and shipping secure, observable AI-augmented software across backend, frontend, and data surfaces. Collaborate with product, design, data, and clinical teams, use experimentation and data to validate impact, and improve engineering practices for safety, reliability, and scale.

Own end-to-end delivery of production ML and LLM-powered systems: design and build training/deployment pipelines, package and deploy models, implement monitoring for performance and drift, support batch and real-time workloads, evolve internal ML platform and tooling, promote best practices, and provide technical leadership and mentoring across data science and engineering teams.

Provide first-line governance for AI systems across lifecycle: assess and test models (including generative and agentic systems), produce technical risk reports and metrics, advise on 'License to Operate' decisions, consult on high-risk system design, and partner with Data Science, ML Engineering, Risk & Compliance to evolve governance standards.

Build and ship production-quality AI applications and reusable deployment assets (agents, orchestration, evaluation). Support enterprise rollouts, integrate with IT/cloud infrastructure, accelerate client adoption, and champion responsible AI while advising and collaborating with client teams.

Lead design and ownership of an end-to-end AI platform for large-scale telecommunications. Define architecture, integration patterns, and standards; guide engineering, ML, and operations teams; ensure scalability, observability, security, and cost efficiency; support vendor evaluations, M&A technical due diligence, and stakeholder communication to deliver measurable business outcomes across multiple European markets.

6–8 week graduate programme training early-career consultants to use AI responsibly in consulting. Develop business analysis, structured problem solving, service design, stakeholder management, project delivery and communication skills while gaining hands-on client experience and continuous professional development.

No job responsibilities provided; the job advert content is missing. Title indicates a senior Data & AI leadership role at Grant Thornton, likely responsible for strategy, delivery oversight, and stakeholder engagement.

Design, build, and maintain scalable, fault-tolerant serving platforms for LLMs. Integrate models for production and research, implement monitoring and observability, perform architecture reviews, and lead cross-functional end-to-end projects enabling LLM capability discovery and high-performance serving.

The AI Engineer will develop and implement agentic AI solutions, drive best practices in engineering, and manage complex platform performance and monitoring.

Lead Citi's AI-First PDLC, defining enterprise capability models, process governance, and AI-enabled standards. Oversee process mapping, AI-augmented LEAN assessments, stakeholder engagement, and build a global team to ensure audit-ready, controlled, and optimized AI-enabled operations.