Top AI Engineer Jobs in London

Lead design and delivery of production LLM-powered agent systems and firm-wide agent standards. Build reference agent apps, connect agents to internal data/APIs, establish security and governance for AI, adopt deployment and observability infrastructure, and partner with stakeholders to identify high-value use cases.

Lead design, build and deployment of production AI workflows embedded with business teams using Microsoft Copilot Studio, Azure AI Foundry and Microsoft Fabric. Own API integrations, CI/CD, MLOps/AIOps, testing, documentation and handover. Provide senior technical leadership, set standards, rapidly prototype in sandbox environments and ensure reliable, secure, production-ready AI solutions connected to enterprise systems (SAP, Salesforce, data lake).

Architect and build agentic workflows combining LLMs, reasoning, and data pipelines. Lead ML/NLP product development from training to deployment, drive experimentation, integrate intelligent agents into recruiting workflows, mentor engineers, and improve reliability, personalization, and observability.

Design, build, and deploy production-ready AI algorithms and high-scale evaluation pipelines focused on LLMs and patent generation. Implement advanced prompt engineering, translate research into product across the stack (Python, Postgres, React/TypeScript), and benchmark models to drive R&D decisions.

As an AI Engineer, you will architect, develop, and deploy scalable AI applications, collaborating with teams to implement AI solutions that drive enterprise productivity and innovation.

Join Abound's one-year Graduate Scheme on the AI Engineering route to build production-grade Generative AI and agent solutions. Work cross-functionally to design prompts, integrate LLMs with tools, APIs and databases, write scalable Python code, and evaluate AI systems. You'll ship code from week one, receive mentorship, and may convert to a permanent role based on performance.

The role requires deploying AI solutions and automations for clients, building prototypes, integrating systems, and collaborating with various teams while ensuring effective client interaction.

Build and ship AI-powered, production-grade features enabling analytics and agentic workflows across large datasets. Rapidly prototype, iterate, and dogfood solutions; integrate agent components with internal tools; collaborate cross-functionally while owning scalable, maintainable AI-driven product capabilities.

The AI Enablement Engineer leads programs for engineering teams to integrate AI, teaches best practices, and develops scalable learning models.

Design, build, and ship production LLM-based systems and agentic workflows. Develop core learning libraries, human-in-the-loop feedback loops, confidence scoring, and scalable agent orchestration. Work with enterprise customers to prototype, deploy, and generalize solutions back into the platform.

Build and deploy AI systems for investment insights, focusing on ML and LLM-driven solutions, while ensuring high-quality data flow and outcomes.

Design, build, and deploy AI-powered solutions (LLMs, RAG, agents) to improve productivity and operational efficiency. Partner with business teams to define use cases, integrate AI with enterprise systems, create production-ready prototypes, and ensure solutions meet governance, security, and responsible AI standards.

Design, build, and integrate generative AI and AI-agent solutions for customer operations, automation, and lending workflows. Prototype quickly, implement Python-based systems, connect LLMs to APIs and databases, design prompts and evaluation processes, and improve production reliability with testing, monitoring, and fallback handling.

Architect and build an AI-native automation and orchestration toolchain (fusing traditional tools with foundation models), enable and mentor early adopter teams, standardize practices across departments, drive infrastructure-as-code and CI/CD adoption, contribute to the IT/Sec operating system, and champion cross-departmental adoption of automation and AI platforms.

Build and own full-stack AI platform systems: backend services, data pipelines, frontends, and cloud infrastructure. Integrate LLM agents with lab data, instruments, and databases; prototype and harden agentic workflows; write well-tested code; mentor teammates and drive engineering best practices in a research-focused environment.

Lead architecture and hands-on implementation of scalable decision automation and production ML systems for Candy Crush Saga. Build reusable decisioning foundations, improve experimentation and causal inference tooling, mentor senior engineers, and ensure reliability, observability, and cross-team alignment for player-facing personalization and adaptation at scale.

Lead design and delivery of production-ready AI integrations to improve developer productivity. Partner with engineering teams to translate AI opportunities into architecture, reference implementations, and handoff-ready proofs-of-concept while ensuring reliability, monitoring, security, and adoption.

Lead design, build, and scaling of AI-powered features across the platform. Prototype, ship, and optimise end-to-end production systems; work closely with clinicians and customers; use analytics and user research to measure impact and prioritise work tied to commercial outcomes.

Lead the development of AI-driven solutions, architect scalable AI products, mentor junior engineers, and integrate AI across the organization.

This role involves designing, building, and operating AI-ready cloud platforms, focusing on public cloud infrastructure and AI service delivery in a regulated environment.

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.

Join Watershed's sustainability AI reporting team to prototype, build, and productionize AI features (LLMs, embeddings) for information extraction and report generation. Ship production-ready, well-tested full-stack code and keep systems current with state-of-the-art AI to deliver customer-facing product features.

Hands-on senior Java engineer to modernise legacy services using AI, implement MCP servers, build AI-assisted CI/CD and developer tooling, and ship production microservices with measurable productivity and performance improvements.

Build and evolve a data and ML platform: implement IaC and CI/CD, develop Python/Spark components, support data/ML lifecycle and AgentOps, enforce security and observability, and collaborate with engineers and scientists to enable platform adoption.

Lead and mentor a team of AI/ML Engineers, developing production-grade AI solutions that extract value from structured and unstructured data, with a focus on customer-centric performance.