Top AI Jobs in London

Own enterprise customers’ AI adoption roadmaps from deployment through expansion. Partner with executives to define AI strategies, coordinate Product, Services, Sales, Success, and partners, and guide solution design and technical escalations. Track adoption, automated resolution, retention, and expansion metrics to drive measurable outcomes. Advise on responsible AI, data governance, compliance, and sustainable implementation while leading success planning, stakeholder alignment, and renewal-focused engagements.

Build and deploy production-grade AI solutions across data ingestion, feature engineering, model training, evaluation, and deployment. Develop LLM integrations, agentic workflows, RAG architectures, dashboards, backend APIs, and prompt-engineering solutions. Collaborate with fixed-income business users, engineering, data engineering, and application support teams. Maintain production applications, write tested and documented code, and evaluate emerging AI/ML technologies.

Leads research and thought leadership on AI-driven HR strategy, transformation, governance, workflow redesign, regulatory environments, and value realization. Produces actionable insights across written, visual, video, and presentation formats; advises CHROs and senior HR leaders; analyzes market trends, vendors, technologies, and client challenges; supports sales and conferences; mentors analysts; and improves research processes.

The Senior AI Agentic Engineer will oversee product strategy and architecture for AI projects, lead cross-functional teams, and manage client interactions across financial services.

Own the end-to-end product lifecycle for AI-enabled manufacturing solutions, including product vision, strategy, discovery, prototyping, roadmap prioritization, launch, adoption, and scale. Partner with customers, engineering, design, sales, marketing, and customer success to deliver measurable operational and commercial value. Identify opportunities to apply AI and automation to manufacturing workflows, support pricing and packaging, and contribute to product growth and P&L outcomes.

Own daily operations and quality assurance for an internal AI-powered growth platform. Test releases, rule changes, edge cases, and regressions; document and verify bug fixes; monitor platform and agent activity; manage approval queues; evaluate agent output; escalate risks; and maintain test plans, QA checklists, runbooks, and release notes.

Senior front-end engineer on an agile Equities Sales & Trading team delivering secure, scalable cloud-native applications. Provides technical leadership, writes and reviews production code, drives design decisions, optimizes UI performance for high-frequency data, and advocates firmwide engineering practices and tools.

Lead development of cloud-native Python applications for global commodities trading desks. Design, build, test, and operate resilient, secure production systems; drive automation and CI/CD; evaluate vendor solutions; and mentor engineering communities of practice.

Lead development and adoption of information architecture standards, controlled vocabularies, and governed data models. Collaborate with business and technical teams to evaluate technologies, provide technical guidance, influence product design, and standardize operational and analytical data management across the Corporate Investment Bank.

Provide empathetic email-based support for Overleaf and Papers AI users, handling technical, billing, and account inquiries. Maintain and create knowledge-base content, report reproducible bugs, refine support processes, and collaborate with Product, Engineering, Sales, Marketing, Customer Engagement, and Finance. Monitor support trends and surface user feedback while helping establish support documentation and playbooks for an early-stage AI product.

Leads the mid-market business development function, including team building, territory strategy, prospecting, negotiation, closing, pipeline management, and account acquisition and retention across pharma and biotech. Partners with marketing, strategy, and commercial operations on go-to-market execution. Represents the company at industry events and develops relationships with clinical trial sponsors while selling complex AI-powered SaaS solutions.

Build and operate data pipelines across Salesforce, HubSpot, Stripe, and Databricks; maintain go-to-market analytics models and dashboards; develop LLM-based agents and automations for Sales, Marketing, and Finance; and productionize integrations with testing and monitoring.

Embeds with hardware engineering teams to build agentic workflows and AI skills that perform engineering tasks, including requirements checks, test-plan analysis, and issue investigation. Demonstrates value to customers, identifies reusable patterns, and translates customer needs into product capabilities. Requires strong Python and data skills, customer-facing communication, modern AI coding-agent experience, and willingness to travel. Hardware, test, controls, forward-deployed, solutions engineering, and security-clearance experience are advantageous.

Leads research, economic modeling, and executive advisory on enterprise AI investments. Develops models for costs, value realization, unit economics, payback, and scenario analysis; benchmarks deployment outcomes; advises CIOs and CFOs on funding, sourcing, scaling, and portfolio decisions; and produces client research, presentations, case studies, and industry guidance.

Support ML engineering teams running production training and inference workloads across Kubernetes, cloud, Linux, and GPU infrastructure. Diagnose distributed PyTorch, CUDA, NCCL, networking, storage, scheduling, and performance issues; analyze observability data; advise customers during incidents; and improve reliability through tooling, automation, documentation, runbooks, and operational processes. The role is hybrid in London, requires at least two office days weekly, and supports EMEA shifts.

Leads customer-facing design and pre-sales architecture for enterprise AI and data solutions using Salesforce Agentforce and Data Cloud. Designs agent interoperability, prompt lifecycles, integrations, governance, data models, and activation strategies. Builds rapid prototypes and demos, facilitates executive workshops, advises on hyperscaler and external agent integrations, and develops technical enablement content. The role also supports early post-sale adoption and serves as a technical resource for internal specialist teams.

Drive AI-led transformation across Legal, People, and FP&A by identifying workflow pain points, mapping end-to-end processes, and building reliable AI agents, automations, integrations, and internal tools. Advise on build-versus-buy decisions and technology stacks while implementing security, privacy, compliance, monitoring, human oversight, evaluation, and audit controls. Partner with frontline teams, executives, and engineers to deliver and continuously improve internal AI products.

Own positioning, messaging, campaign narratives, and copy across B2B payments content. Build AI-assisted copy frameworks, prompts, templates, and tone-of-voice guardrails; edit AI output for accuracy and brand alignment; lead thought leadership; support PR and sales enablement; and collaborate with legal, compliance, product marketing, and brand teams. Manage studio intake, briefing, and prioritization while using performance insights and Gen AI experimentation to improve content quality and efficiency.

Build and deploy production machine learning systems for classification, retrieval, ranking, personalization, and evaluation. Develop training and inference pipelines, improve model quality and efficiency, create evaluation datasets and metrics, monitor production performance, debug issues, and ship reliable ML-powered features. The role emphasizes balancing accuracy, relevance, latency, cost, privacy, and reliability while collaborating with engineering and product teams.

Build AI proofs of concept and production-ready solutions for clients, using Python and AI coding tools. Lead technical discovery, testing, security, monitoring, cost control, DevSecOps, and FinOps practices. Coordinate distributed partner and internal delivery teams, review technical work, manage risks and budgets, and explain solutions to non-technical stakeholders. Support proposals while ensuring responsible AI, data protection, and secure handling of client information.

Lead AI consulting engagements by interviewing clients, facilitating workshops, shaping AI strategies, prioritizing use cases, developing business cases, and creating implementation plans. Manage client relationships, coach delivery teams, oversee scope, budget, quality, and timelines, and support proposals. Apply generative AI tools to produce client deliverables while ensuring accuracy, responsible AI practices, data protection, adoption planning, and compliance with UK GDPR.

Lead technical design and delivery of AI-native email applications. Own architecture and implementation across backend, frontend, APIs, databases and cloud. Stay hands-on writing and reviewing code, debugging production, mentoring engineers, driving major initiatives, improving tooling, and ensuring reliability, security and observability while collaborating with product, design and AI teams.

Build and operate a production Azure data and AI platform. Responsibilities include Azure landing zones, network architecture, identity, governance, monitoring, Databricks and Unity Catalog administration, data integrations, MLOps pipelines, model deployment, CI/CD, infrastructure as code, security scanning, self-service tooling, documentation, and on-call support. The role requires hands-on Terraform, Azure, Databricks, containerization, observability, and Python or PowerShell experience.

Build and operate an Azure-based data and AI platform through hands-on infrastructure engineering. Responsibilities include designing Azure landing zones and networks, managing governance and identity, deploying Databricks and data services, building MLOps pipelines, implementing DevSecOps practices, creating self-service tooling, and supporting platform incidents. The role requires Terraform, Azure, Databricks, CI/CD, containerization, security, observability, and Python or PowerShell expertise.

Own and build Oversee’s product marketing function, including ICP definition, positioning, persona messaging, competitive and win/loss research, product content, sales enablement, and end-to-end product launches. Partner closely with product, sales, growth marketing, and executives to translate technical AI and travel technology capabilities into compelling commercial messaging, establish repeatable GTM processes, and measure launch performance.