Top AI Engineer Jobs in London

Own AI Finance product areas across insurance, payments, savings, investing, travel, and related financial services. Define user problems, product scope, success metrics, roadmaps, and launch plans while collaborating with engineering, design, data, operations, compliance, and business teams. Simplify complex financial workflows, launch features, monitor performance, analyze feedback and metrics, and drive continuous improvements while balancing speed, usability, business impact, compliance, and operational complexity.

Deploy and integrate Latent Labs' generative biology models into customer environments. Lead end-to-end technical deployments, build production-grade APIs, data pipelines and model-serving infrastructure, work on-site with pharma/biotech partners, ensure enterprise security and reliability, gather customer feedback, and document integrations while collaborating with product and research teams.

Owns the end-to-end definition, evaluation, and quality of AI-powered email capabilities. Partners with ML, backend, client, and product teams to translate model behavior and technical constraints into requirements, system decisions, experiments, and roadmaps. Balances quality, latency, cost, safety, reliability, and user experience while identifying failure modes and building trustworthy, predictable AI features.

Lead the architecture and hands-on development of reliable AI-powered email applications across backend, mobile, and desktop platforms. Design orchestration, state management, tool use, retries, fallbacks, and failure handling for long-running workflows. Build production APIs, improve observability and reliability, make trade-offs involving latency, cost, security, and user experience, and lead a small senior engineering team through technical reviews, mentorship, and execution.

Leads application engineering for an AI-powered email product across backend, mobile, and desktop platforms. Designs reliable AI workflows involving orchestration, state, tool use, retries, fallbacks, and failure handling. Builds APIs connecting AI capabilities to product experiences, makes architectural trade-offs, establishes observability and operational excellence, and leads a small senior engineering team through mentorship, design reviews, and code reviews. Partners with ML leadership to deliver scalable, reliable, and maintainable production systems.

Lead application engineering for an AI-powered email product across backend, mobile, and desktop platforms. Design reliable AI workflows involving orchestration, state, tool use, retries, fallbacks, and failure handling. Build APIs connecting AI systems to product experiences, make architecture and scalability decisions, mentor senior engineers, and establish strong observability, monitoring, testing, security, and operational practices. Partner with ML leadership to ship fast, reliable, maintainable product experiences.

Own product areas within AI Stockbroking and broader financial services. Define user problems, product direction, scope, metrics, roadmaps, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams. Simplify complex financial products, use research and performance data to guide decisions, launch features quickly, and drive continuous post-launch iteration while balancing user experience, business impact, compliance, and operational complexity.

Own product areas within BJAK’s AI Neobank across financial services such as insurance, payments, savings, investing, and travel. Define user problems, product direction, roadmaps, metrics, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify complex financial journeys; and use research, feedback, experiments, and performance data to drive continuous improvements.

Own product areas across AI investing and other financial services. Define user problems, product scope, success metrics, roadmaps, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify complex financial products; launch features; monitor performance; and iterate using data, feedback, research, and production insights.

Own AI Finance product areas across insurance, payments, savings, investing, travel, and other financial services. Define user problems, product scope, metrics, roadmaps, and launch plans; collaborate with engineering, design, data, operations, compliance, and business teams; simplify complex financial journeys; launch features; monitor performance; and iterate using data, feedback, research, and production insights.

Own AI engagement outcomes from success definition through production delivery. Design and evaluate end-to-end AI and GenAI systems, manage technical risk and trade-offs, communicate with clients, lead code reviews, resolve technical disagreements, mentor engineers, and develop reusable components and best practices. Deploy models and LLMs on AWS or Databricks with strong testing and CI/CD practices.

Build, deploy, and maintain AI-powered web applications for government and industrial customers. Work end-to-end: define product specs with customers, implement front-end and back-end systems, integrate data pipelines and sensor networks, optimize performance and reliability, and support deployed systems globally. Collaborate with cross-functional teams, mentor junior engineers, and travel on-site to deliver production-grade solutions.

Deploy and optimize LLM-based solutions for real-world government, healthcare, and energy problems. Build scalable data pipelines, productionize models (RAG/agents), monitor performance, engage stakeholders including government officials, and collaborate with cross-functional teams to deliver impactful AI products.

Design, train, evaluate and deploy robust deep learning models from sensor input to user interface; integrate models into cloud and edge systems; work with customers to define problems; document, monitor and ensure safe, secure production AI solutions.

Validate and automate testing for AWS serverless, event-driven and modernized applications. Design and run functional, integration, contract, performance, resilience and security tests. Build automated test frameworks, integrate with CI/CD, leverage AWS Kiro for AI-assisted test generation and validation, and support migration/regression testing across microservices.

Build and maintain product features that streamline property transactions. Use LLMs and AI coding agents to accelerate development, debug issues, and iterate rapidly. Own tasks from day one, collaborate with a small team, and apply practical problem-solving to deliver customer-facing improvements.

Design, build, and maintain Python-based AI applications and intelligent workflows. Integrate AI/ML models and LLM APIs, implement APIs and microservices, optimize data pipelines, ensure scalability, security, CI/CD, and collaborate across teams to deliver production-grade AI features.

Work with enterprise customers to identify problems, design and build LLM-powered agentic systems, ship solutions to production, drive adoption, and contribute reusable context-learning and evaluation components to the platform.

Define and build Fuse's inference-serving architecture and stack, including request routing, batching, scheduling, and autoscaling for latency-sensitive workloads. Own model-level optimizations (quantisation, distillation, speculative decoding), translate performance SLAs into capacity plans, and integrate low-level GPU/CUDA performance work with serving infrastructure. Set benchmarks, tooling, and act as technical owner of inference performance and reliability.

Design, build, test and deploy machine learning and generative AI models for production; prepare and refine datasets; integrate AI services with applications and cloud; document models, pipelines and APIs; monitor, evaluate and improve model performance; collaborate with engineers, data specialists and business teams.

Contract AI Engineer to build an enterprise AI capability: design agentic workflows and Copilot extensions across Microsoft 365 and Salesforce, build production-ready agent solutions (connectors, monitoring, cost controls), translate sales methodology into real-time coaching and CRM insights, and collaborate with GTM leadership to drive behaviour change. Hybrid role in London for 4-6 months (3-4 days/week).

You will design, build and refine AI systems for legal applications, focusing on LLM agents and data evaluation frameworks.

Develop scalable cloud infrastructure and robust software solutions for AI/ML, collaborating with engineers and scientists to enhance scientific research.

Uncountable seeks a Platform Engineer focused on Generative AI to develop AI-powered tools for scientific R&D, requiring fullstack development skills and a passion for advancing user needs.

Develop intelligent agents for a materials discovery engine, orchestrating scientific workflows and integrating experimental design for sustainability solutions.