Top AI Engineer Jobs in London

Leads software engineering strategy and development for AI/ML data platforms. Designs reusable frameworks, architects governed agentic AI development workflows, builds secure and scalable cloud-native applications, and operates 24x7 tier-1 services. Influences technical, product, and business leaders; drives adoption of engineering methods, responsible AI controls, automation, and platform capabilities across teams.

Lead the design, development, deployment, and continuous improvement of AI/ML solutions for enterprise customers. Build integrations using Python or JavaScript, APIs, microservices, and enterprise systems; analyze data; automate workflows; address AI risk, privacy, and compliance; and align technical solutions with business goals. Mentor engineers and communicate effectively with technical and non-technical stakeholders in complex, ambiguous environments.

Build and operate production AI and rule-based agents for lead generation workflows. Responsibilities include integrating agents with data warehouses, internal services, and APIs; implementing structured outputs, validation, permissions, guardrails, audit logging, autonomy controls, evaluation, tracing, failure handling, and performance monitoring. Collaborate with operators, data scientists, product managers, and users to deliver dependable, measurable, and commercially valuable agent systems.

Build, deploy, and maintain full-stack AI applications, services, and data pipelines for enterprise use. Collaborate with data scientists, machine learning engineers, research scientists, and technical teams to productionize AI products. Develop secure, scalable, resilient software; maintain APIs and backend services; conduct code reviews; troubleshoot complex issues; apply CI/CD and software security practices; and support LLM applications using retrieval-augmented generation and prompt design.

Develop and deploy production AI/ML applications for digital banking, using Python, SQL, cloud platforms, LLM patterns, classical machine learning, and data analysis. Responsibilities span requirements gathering, solution design, prototyping, evaluation, deployment support, monitoring, governance, and iteration. The role collaborates with product, operations, marketing, and technical teams to deliver customer and operational improvements while supporting risk, privacy, and model controls.

Leads the design, productionization, and operation of LLM-powered agentic commerce applications. Builds retrieval systems, agent memory, organizational context, multi-agent workflows, evaluation frameworks, guardrails, and ML pipelines. Deploys resilient, observable solutions on AWS or Azure using MLOps practices, while partnering with product, business, data science, and engineering stakeholders to deliver secure, auditable production agents.

Lead AI architecture and engineering for enterprise agentic AI systems. Define standards for RAG, agents, orchestration, evaluation, security, and governance; provide hands-on technical leadership, mentor engineers, and align cross-functional stakeholders to deliver secure, compliant AI solutions.

Build full-stack AI-enabled applications and backend services integrating MCP servers, LLMs, RAG retrieval, and agent orchestration. Design reusable MCP tools/skills, implement secure RBAC, ensure observability, and prepare deployments for OpenShift/Kubernetes while collaborating across product and platform teams.

Design and deliver enterprise-grade machine learning systems and AI-powered applications for banking. Build scalable, secure, distributed applications and automated cloud, desktop, and ML pipelines. Apply MLOps practices for versioning, reproducibility, and observability while collaborating with cloud and site reliability engineering teams. Develop reusable libraries and business-critical, data-intensive solutions, aligning machine learning problems with business objectives. The role may include mentoring or people leadership.

Leads a team building secure, production-grade AI-driven software systems. Responsibilities include software architecture, Java or Python development, AI/ML integration, LLM and generative AI applications, algorithm validation, automated testing, CI/CD, cloud-native development, troubleshooting, and operational reliability. The role also establishes responsible AI practices, validates AI-assisted engineering outputs, mentors engineers, translates business requirements into technical roadmaps, and ensures timely delivery through Agile methodologies.

Develop and deliver production AI/ML solutions across the full lifecycle using Agile methodologies. Translate business requirements into technical specifications, build secure production code and algorithms, create architecture artifacts, analyze large datasets, develop visualizations, troubleshoot systems, and identify improvements. The role requires expertise in machine learning, Python, Spark, distributed computing, AI frameworks, agentic workflows, and AWS services, while contributing to engineering communities and emerging technology initiatives.

Build, deploy, and maintain machine learning models for Snapchat products. Apply ML techniques to real-world problems, contributing across data analysis, training, evaluation, and production deployment. Collaborate with cross-functional teams to launch scalable ML-driven features using modern AI tools and engineering workflows while maintaining high standards for correctness, security, and production quality.

Lead Thoughtworks’ UKI Data and AI practice, driving complex deal solutioning, pricing, delivery outcomes, sales pipelines, and P&L performance. Build and scale teams, develop production AI and agentic delivery capabilities, manage executive client relationships, and advise on AI strategy, governance, risk, and regulation. The role combines technical architecture expertise with commercial leadership, team development, capacity planning, and strategic workshop facilitation.

Lead the architecture, development, deployment, and scaling of production-grade agentic AI systems for banking workflows. Build AI agents with Python, ADK, LangGraph, and Arc; integrate enterprise APIs, databases, microservices, and Kafka; and implement secure execution environments. Provide technical leadership through architecture and code reviews, security assessments, mentoring, and reusable enterprise platform contributions. Ensure solutions meet reliability, regulatory, governance, and responsible AI requirements.

Designs, deploys, and operates secure, production-grade AI services, LLM workflows, data pipelines, and reusable platform components on Azure and Databricks. Owns CI/CD, infrastructure as code, APIs, observability, incident response, and operational support. Collaborates across engineering, data science, security, infrastructure, product, and business teams while contributing to architecture, standards, platform strategy, mentoring, and technical enablement.

Build and ship AI-powered features for requirements engineering, including drafting, consistency checks, impact analysis, intelligent suggestions, and agentic workflows. Integrate and evaluate language models while optimizing reliability, latency, cost, observability, and safety. Develop supporting data pipelines, evaluation systems, prompt and model management, and backend and UI integrations. Collaborate with product teams and customers to prototype, launch, and iterate on production AI capabilities.

Leads strategic customer engagements focused on application modernization, AI, agentic solutions, and developer technologies. Designs architectures, demonstrations, proofs of concept, pilots, and hackathons using Azure, Microsoft Foundry, GitHub, containers, Kubernetes, APIs, data, and AI. Builds technical strategy, orchestrates Microsoft and partner resources, resolves technical blockers, mentors peers, develops reusable assets, and provides customer and market feedback to product and engineering teams.

Build and deploy production AI agents that automate electronics manufacturing, factory operations, supplier workflows, finance, and software development. Responsibilities include identifying manual processes, developing end-to-end automation, improving agent infrastructure, creating evaluations, ensuring reliability and idempotency, and collaborating with factory and business teams. The role emphasizes strong software engineering, LLM production experience, classical computer vision and optimization, secure sandboxing, and measurable accuracy and cost improvements.

Lead development of high-performance Java applications for real-time FX risk management. Responsibilities include architecture, low-latency distributed systems, JVM optimization, coding, AI-assisted development, code reviews, testing, cloud deployment, production support, release ownership, and technical leadership without direct reports. The role works closely with quantitative analysts, traders, and business stakeholders while contributing to global risk technology strategy.

Build and productionize secure AI applications from prototypes, including Python services, APIs, data models, MCP integrations, testing, deployment, monitoring, authentication, and support. Evaluate AI behavior, manage production reliability and costs, and collaborate with Technology, security, infrastructure, and product teams. The role requires pragmatic engineering judgment, strong Python and SQL skills, and experience taking incomplete applications into production.

The role involves designing, developing, and supporting cloud and AI solutions primarily on Azure, including automation and integration components, while ensuring security and compliance.

Develop and support secure, scalable software for AI/ML data platforms. Responsibilities include Python architecture and development, cloud-native and containerized application delivery, code review, testing, troubleshooting, operational stability, and automation of recurring remediation. The role also promotes validated AI-assisted engineering practices and collaborates with product managers and data strategy teams. Preferred experience includes graph databases such as AWS Neptune and generative AI engineering pipelines.

Lead security investigations for AI systems used in scientific discovery. Develop threat models, test defenses through adversarial assessments and technical analysis, define security requirements, evaluate evidence, and drive improvements with Security, Safety, and engineering teams. The role addresses dangerous misuse, model and data compromise, deployment risks, and safeguards for increasingly capable AI systems.

Build and maintain software, services, data pipelines, and model integrations for AI safety evaluations, red teaming, mitigation experiments, and scientific AI research. Translate researcher needs into reliable tools, preserve reproducible experiment records, and own systems from prototyping through deployment, testing, monitoring, documentation, and improvement. Collaborate with AI and software engineering teams on shared infrastructure.

Develop and implement safety mitigations for frontier scientific AI models, agents, tools, and laboratory-connected workflows. Translate risk assessments and policy requirements into safeguards spanning training, classifiers, monitoring, access controls, and runtime interventions. Design experiments, evaluate robustness and legitimate-use impacts, investigate failures and distribution shifts, and integrate mitigations with model training, inference, platform, and evaluation teams.