Top AI Engineer Jobs in London

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.

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.

Build and deploy LLM-powered workflows for bioinformatics and metabolomic chemistry. Translate scientific questions into reliable AI tasks, query biological data and knowledge graphs, generate traceable recommendations, and develop benchmarking and evaluation infrastructure. Collaborate with wet-lab scientists, chemists, microbiologists, and engineers while contributing Python code, data architecture, pipelines, and model deployment to a shared production codebase.

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.

Build and ship AI voice-agent products across Python, TypeScript, cloud infrastructure, and event-driven systems. Design agent orchestration, prompting, tool calling, and speech pipelines; create evaluations, monitoring, and feedback loops; improve reliability; and work directly with customers to scope and deliver solutions. The role also involves full-stack development, third-party integrations, technical ownership, and establishing engineering best practices.

Build and deploy production-grade AI solutions for investment, client, and operational workflows. Responsibilities include integrating LLMs, developing automations and agentic workflows, connecting enterprise data sources, prototyping and deploying solutions, partnering with stakeholders and architects, contributing to AI platform standards, training users, and ensuring responsible AI governance and compliance.

Lead the design, build, and deployment of AI-powered agents, workflows, and integrations for global marketing. Evaluate buy-vs-build, integrate platforms (HubSpot, Salesforce, Meta, Google, LinkedIn), optimize AI economics, and measure ROI while collaborating with marketing teams to automate and improve campaign execution and content localization.

Lead technical direction for an AI platform connecting workloads to multiple LLM providers. Build API gateway and routing infrastructure, agent orchestration, capability lifecycle tooling, access controls, API key management, caching, cost and latency optimization, adoption analytics, and trust, safety, and governance controls. Define API standards and promotion workflows while supporting auditable, scalable AI services across client engagements.

Build and ship optimization solutions for IFS PSO, translating customer scheduling and planning problems into models, configuring and debugging the scheduling engine, and developing solutions against live datasets. The role combines C#/.NET software engineering, applied optimization, customer engagement, and product feedback. Engineers will work with Angular and TypeScript, diagnose performance issues, deliver under tight deadlines, and use AI-assisted workflows to accelerate solution development.

Forward Deployed AI Engineers develop Gen AI solutions by building workflows, implementing strategies, and solving real-world problems at scale with clients.

Design, build, test, and deploy scalable AI services integrated with operational systems. Develop Python services, REST APIs, ETL/data pipelines, IaC, and monitoring; collaborate with AI specialists and operations stakeholders to improve workflows, controls, and production readiness in Markets Operations.

AI/ML Engineers will build and deploy AI-native products, autonomous agents, LLM applications, RAG systems, and production ML pipelines. Responsibilities may include integrating AI into crypto infrastructure and consumer products, fine-tuning and evaluating models, optimizing inference, and developing agentic workflows and machine-to-machine payment systems. Engineers will work end-to-end with high-ownership teams in fast-moving environments.

Lead end-to-end delivery of AI Agents on the Moveworks ServiceNow platform: design, build, integrate, tune, and launch custom LLM-based automation solutions while partnering with customers, product, and engineering to drive adoption and product feedback.

Deliver hands-on GenAI implementations for international government customers, from identifying mission needs through architecture, prototyping, evaluation, production readiness, adoption, and handoff. Partner with agencies, technical teams, systems integrators, cloud providers, and senior stakeholders while addressing privacy, data residency, security, identity, procurement, and regulated-environment requirements. Build applications with OpenAI technologies, contribute developer resources, share best practices, provide product feedback, and manage multiple concurrent customer engagements.

Build and improve production AI systems, including chatbots, autonomous account actions, document extraction pipelines, and LLM-driven case-management workflows. The role covers discovery, prototyping, deployment, evaluation, monitoring, prompt design, retrieval, agent workflows, and identifying manual processes suitable for automation.

Lead development and enhancement of equity derivatives trading and analytics applications, drive automation and AI-assisted engineering practices, collaborate with trading and quant teams, maintain global platform stability, and lead the Systematic Quoting Platform workstream for EMEA.

Design, build, and deploy scalable ML services combining NLP, CV, and statistical methods. Collaborate with engineers, cloud and SRE teams to productionize models, ensure observability and compliance, and maintain models over time. Communicate results to stakeholders and document processes for regulatory needs.

Design, build, and productionize autonomous and assistive multi-agent AI systems. Implement RAG and semantic search with vector databases, integrate tools/APIs, apply prompt engineering and guardrails, orchestrate microservices workflows, instrument agents for metrics, run evaluations/A-B tests, and mentor teammates.

Lead design, development, testing, release, and production support for Equity Derivatives workflows. Own production outcomes, incident triage, stakeholder communication, UAT, architecture and observability. Drive adoption of AI-assisted engineering practices, maintain release discipline, gather telemetry for stability metrics, and improve system reliability and coding hygiene through cross-functional collaboration.

Lead design and delivery of high-performance, real-time data pipelines and analytics for trading. Drive AI-enabled data engineering, mentor engineers, manage global initiatives, and improve SDLC, testing, and operational reliability.

Design, build, and deploy production-grade generative AI and multi-agent systems for financial services. Architect scalable MLOps pipelines, optimize LLMs and multi-modal models, implement RAG and prompt-engineered agent workflows, and collaborate with clients and cross-functional teams to accelerate GenAI adoption and deliver measurable business impact.

As a Lead AI Engineer, you'll architect and deploy generative AI systems, optimize large models, and lead teams in AI engineering across financial services.

Own the production improvement loop for an AI Context Engine, improving agent behavior, retrieval, memory, telemetry, evaluation, and safe rollout. Build production services in TypeScript or Python, design observability and evaluation systems, analyze agent traces and customer feedback, and partner with data scientists, backend engineers, product, and UX. Mentor engineers, review designs, and shape the technical roadmap for agentic products with public APIs and evolving data models.

Own Sesame’s end-to-end product experience across UX, interaction logic, visual consistency, and front-end implementation. Build dense financial interfaces, contribute to the coded design system, establish standards for AI-assisted development, and work directly with investment professionals and advisors. The role combines front-end engineering, product design judgment, user feedback, design-system leadership, and review of others’ work.

Lead the design, development, deployment, and support of Microsoft Power Platform and AI solutions. Build secure AI agents and agentic workflows using trusted knowledge, tools, approvals, MCP services, and human hand-offs. Integrate solutions with Azure, Microsoft 365, enterprise systems, and APIs. Establish engineering standards for testing, CI/CD, observability, security, and release management while mentoring developers and supporting live services.