Top AI Jobs in London

Lead Optasia’s company-wide AI enablement and adoption strategy. Design and deliver generative AI training, develop an AI Champions network, coach employees, identify practical AI use cases, and promote responsible adoption. Own AI communications, learning resources, adoption metrics, and business-impact reporting while partnering with IT, Security, and cross-functional stakeholders. Build practical AI assistants, workflows, and agents using tools such as Claude and Microsoft Copilot.

Build and ship agentic LLM systems for Treasury: cash monitoring, funding execution, narrative generation, and discovery-to-deployment pipelines. Create reusable agent infrastructure, governance, evaluation, and partner with engineering to ensure data accuracy while embedding with operators to scope and iterate solutions.

Own strategy and execution for AI-powered products, including conversational AI, agentic workflows, legal document extraction, and covenant analysis. Partner with ML engineers, data scientists, legal experts, and customers to define roadmaps, evaluate probabilistic model outputs, improve data and feedback loops, and ensure accuracy, explainability, and responsible AI practices. Lead an engineering squad while translating technical capabilities into value for financial and legal professionals.

Build and evolve 9fin’s AI platform from the ground up, supporting generative AI, agentic AI, RAG, model serving, embeddings, vector search, and intelligent automation. Develop APIs, SDKs, orchestration layers, deployment pipelines, observability, evaluation, and governance capabilities. Partner with AI and backend engineers to establish scalable architecture, developer tooling, operational standards, and reliable production AI services.

Design and build generative AI applications for complex financial and legal workflows. Lead the full model lifecycle from experimentation and training through testing, monitoring, and deployment. Apply advanced techniques across information retrieval, knowledge graphs, recommendation systems, statistical modeling, and GenAI. Collaborate cross-functionally, translate business problems into scoped initiatives, mentor teammates, promote AI adoption, and deliver production-ready solutions with strong product impact.

Build production generative AI systems for complex financial and legal workflows. Responsibilities include document understanding, structured information extraction, model lifecycle management, retrieval and context engineering, data validation, evaluation, deployment, and monitoring. The role requires collaboration across teams, mentoring, product focus, and applying advanced AI research to convert legal and financial documents into reliable structured data.

Lead and advise on large-scale technology procurements, outsourcing, cloud/SaaS, AI and digital transformation projects. Draft and negotiate complex technology and commercial contracts, manage multi-stakeholder, multi-jurisdictional matters, supervise junior lawyers, build client relationships, and support business development and market engagement.

Provides technical consulting for Zendesk AI implementations, configurations, integrations, optimization, and adoption. Leads AI projects end to end, manages scope and timelines, trains stakeholders, supports change management, removes technical blockers, and aligns solutions with measurable customer outcomes. Partners with Customer Success and Sales to develop AI roadmaps, improve adoption, and support retention and expansion through analytics, success plans, and executive communication.

Own Togather’s brand direction, strategy, messaging, content calendar, organic campaigns, and flagship thought-leadership content. Build relationships with major clients, venues, festivals, partners, investors, media, and suppliers. Collaborate on PR, value propositions, commercial pitches, and lead acquisition strategy while maintaining brand standards. The role requires hands-on photography and videography, strong campaign planning, senior stakeholder management, and the ability to turn brand strategy into fast, effective output.

Build production-ready AI agents and automations using n8n and LLMs to integrate systems via APIs/webhooks, use SQL and code (Python/JavaScript) to prototype, deploy and iterate autonomous workflows that reduce manual work across Sales, Operations, Supply Chain, Finance and Product.

Leads and scales Databricks’ EMEA Forward Deployed AI Engineering team, overseeing hiring, mentoring, customer relationships, strategic AI professional services engagements, delivery processes, account outcomes, and cross-functional collaboration with Sales, Product, Research, Engineering, and GTM teams. The role guides enterprise customers through AI/ML transformations, develops scalable engagement models, manages executive stakeholders, and drives production ML and GenAI solutions in cloud environments.

Conduct research on real-world BGP usage and Internet routing behavior using large-scale data, statistics, data science, and AI/ML techniques. Analyze routing dynamics and Internet topology, prototype algorithms and tools, collaborate with researchers, engineers, and product teams, and contribute to production systems, technical reports, and research publications.

Build Python-based backend services, APIs, and integrations that embed AI capabilities into a SaaS platform. Develop scalable customer-facing features, connect AI outputs to workflows and dashboards, collaborate with product, design, and platform teams, and implement observability, monitoring, and error handling. Contribute to the AI integration stack, including SDKs, MCP servers, and LLM orchestration.

Designs and deploys autonomous AI agent ecosystems and LLM-powered workflows using leading orchestration frameworks. The role includes enterprise architecture, agent memory and tool integration, client workshops, pre-sales and RFP support, solution blueprints, cost estimation, reusable accelerators, AI governance, and technical mentorship. Candidates collaborate with consulting and domain teams to deliver client-centric AI solutions across industries.

Design, train, fine-tune, and deploy production-grade LLMs and AI pipelines. Optimize inference for low-latency and high-throughput, implement quantization/caching/batching, build real-time pipelines (WebSockets/SSE), manage containerized deployments (Docker/Kubernetes), and collaborate with stakeholders to deliver scalable RAG, voice, and document-intelligence solutions.

Design, deliver, engineer, govern, and support AI applications, agents, BI solutions, and data integrations. Collaborate with stakeholders to define requirements, create technical designs and test plans, manage third-party delivery, evaluate emerging technologies, and ensure solutions meet architecture, compliance, supportability, and lifecycle standards. Use AI-assisted development and Azure services to simplify processes, improve workflows, and reduce support overhead.

Designs, builds, and maintains AWS and on-premises infrastructure for machine learning engineers, data scientists, and software teams. Responsibilities include operating Kubernetes and MLOps platforms, responding to service requests, troubleshooting production issues, implementing infrastructure as code, improving reliability and developer experience, and supporting secure, scalable product delivery. The role requires cross-team collaboration, clear technical communication, and commuting to London two days per week.

Own Soda’s technical content function across its website, blogs, webinars, videos, and audience growth. Build automated, agentic LLM content pipelines with structured outputs and evaluations while maintaining strong editorial judgment, distinct voices, and high-quality technical writing. Develop independently using Python or TypeScript, APIs, webhooks, and databases. Translate data quality and infrastructure concepts for technical audiences, collaborate with founders and customer engineers, and continuously improve workflows based on content performance.

This role invites interested candidates to express their interest for future opportunities, emphasizing exceptional talent and a collaborative team environment dedicated to AI advancements.

This position invites individuals to express interest in future roles at Unlikely AI, focusing on innovative AI technology development and collaboration.

Join the Data Science team to innovate solutions for data problems, develop forecasting models, and enhance quant strategies using machine learning.

Design and deliver secure, scalable Azure data platforms for global enterprise customers. Lead architecture engagements, roadmaps, proofs of concept, pilots, and production deployments across Microsoft Fabric, Azure Databricks, databases, governance, and AI workloads. Own technical delivery, workload health, resiliency, security, capacity optimization, and adoption outcomes. Resolve technical blockers, collaborate with product and engineering teams, develop reusable implementation assets, and mentor others while enabling Azure consumption and production-scale AI scenarios.

Designs and operates secure, scalable AWS infrastructure and reusable Terraform modules. Owns CI/CD, GitOps, observability, reliability, automation, and cost optimization. Builds production platforms for AI agents and LLM-backed services, including identity, access controls, secrets, evaluation, regression testing, and operational safeguards. Mentors engineers, communicates technical trade-offs, and supports broader platform and developer-experience strategy.

Develop agentic LLM workflows and contribute Python code to support decision-making in bioinformatics and metabolomic chemistry. The intern will design workflow architectures, prompts, tool integrations, benchmarks, tests, and evaluation processes using proprietary biological data. This applied research role involves collaboration with bioinformaticians, data scientists, wet-lab scientists, automation scientists, and software engineers, with opportunities to deploy production code used by scientists. The placement is fully immersive and on-site in London for at least three months.

Design and build production-grade AI platform capabilities for agent-based products. Develop multi-agent prompts, orchestration patterns, tool and function-calling integrations, APIs, testing strategies, evaluation tooling, and solution designs. Select and optimize models and frameworks for quality, latency, scalability, and cost. Monitor and improve agents in production, integrate them end to end, develop prototypes, and collaborate with platform and product engineers on reliable user-facing experiences.