Top AI Engineer Jobs in London

Leads the technical direction and delivery of enterprise AI programs, designing agentic systems, multi-agent workflows, RAG pipelines, and generative AI applications. Builds production Python services, integrates databases and LLM tooling, establishes AI governance and observability, and partners with platform engineering on deployment. Translates enterprise needs into AI roadmaps, provides architectural oversight, mentors engineers, codifies reusable patterns, and communicates outcomes to senior stakeholders.

Sets technical direction for data and AI capabilities, leads large-scale production solutions, establishes engineering standards and reusable frameworks, improves AI monitoring and evaluation, partners with senior stakeholders, mentors engineers, and supports responsible AI adoption across the organization.

Build modular, config-driven AI video generation pipelines for mobile gaming advertisements. Integrate fal.ai models and APIs, orchestrate reliable asynchronous jobs, manage retries and resumable execution, track costs, and use FFmpeg for video assembly, subtitles, overlays, audio mixing, and format conversion. Document workflows so marketing teams can operate them without coding, while collaborating with creative specialists and demonstrating a working pipeline.

Build foundational systems for AI models across data modalities, develop agentic systems and specialized models, and implement reinforcement learning and active inference solutions. The role involves hands-on architecture and product development, requiring Python backend expertise, cloud deployment knowledge, DevOps practices, and the ability to contribute across the technical stack in an early-stage startup.

Build and scale a distributed platform supporting engineering simulations, agentic applications, and physical AI workflows. Responsibilities include designing distributed architecture, developing production Python services, optimizing distributed compute, implementing workflow orchestration, and contributing to CI/CD, testing, deployment, observability, reliability, and incident response. The role also involves mentoring engineers, making technical decisions, collaborating cross-functionally, interviewing candidates, and supporting end-to-end platform operations.

Lead the architectural evolution of an AI platform from monolithic to distributed systems. Design agent-ready services, write production Python, build distributed compute and workflow orchestration, and own CI/CD, testing, deployment, observability, reliability, and scaling. Mentor engineers, establish technical standards, guide cross-team decisions, collaborate with stakeholders, and support hiring. The role requires deep distributed systems, Kubernetes, AWS, workflow orchestration, data processing, security, and production reliability expertise.

Lead end-to-end AI and technology consulting engagements, from opportunity shaping and client advisory through delivery governance and team leadership. Design and deliver production-grade AI/ML, LLM, software, and cloud solutions, including agentic systems, MLOps, monitoring, cloud architecture, APIs, front-end applications, testing, security, and CI/CD. Apply deep technical expertise while communicating effectively with technical and non-technical stakeholders and supporting practice growth.

As an Applied AI Engineer, you will build AI applications for public sector clients, create and evaluate datasets, and collaborate with teams to address challenges using AI solutions.

Design, build, and maintain AI agents and automated workflows for marketing. Identify automation opportunities, deploy and monitor agents, integrate with marketing tools, and report efficiency and business impact. Ensure data quality, privacy, and documentation, and enable marketing teams to adopt automations.

Lead large-scale technical initiatives and build scalable product functionality and infrastructure using TypeScript, React, and Node.js. Partner with product and design teams to develop privacy features for SaaS customers, make architectural tradeoffs, mentor engineers, support hiring, and establish engineering excellence across a growing organization.

Build and deploy production AI products for legal services, including agentic workflows, retrieval, evaluation harnesses, observability, APIs, integrations, and user interfaces. Develop maintainable Python and TypeScript code, implement secure enterprise patterns, and take AI features from prototyping through controlled production. Measure and improve quality, reliability, latency, and cost through testing, tracing, monitoring, and user feedback. Collaborate across product, data, technology, security, and multidisciplinary R&D teams.

Lead customer engagements building and productionizing AI and generative AI applications on Databricks. Responsibilities include architecture, technical delivery, client advisory, team leadership, workshops, MLOps, production pipelines, stakeholder communication, and technical enablement. The role requires strong Python, cloud, MLflow, Databricks, PySpark, SQL, Delta Lake, governance, LLM, and modern AI framework expertise in a consulting environment.

Develop and post-train deep learning models while building systems, tooling, and workflows for training, evaluation, debugging, and deployment. Contribute to open-source projects, distributed AI infrastructure, backend services, and developer platforms. Collaborate with research, product, infrastructure, and customer teams to solve complex technical problems, prototype ideas, and productionize successful experiments.

Design and build end-to-end software primitives for AI infrastructure: domain modeling of power/datacenters/chips, authorization, audit, orchestration, vendor data ingestion (KMZ/BIM/Revit), and agent integration to enable safe, auditable agent operators. Ship production-quality systems and work across ambiguity to turn unstructured data into durable, composable entities.

Designs QA strategies, evaluation frameworks, quality metrics, and automated testing systems for GenAI and agentic applications. Develops Python-based end-to-end tests across APIs, UIs, performance, prompts, and regression scenarios. Performs adversarial testing for hallucinations, bias, and security issues; builds monitoring and alerting systems; maintains golden datasets; and establishes testing best practices for reliable, safe, and trustworthy AI features.

Develop automated QA strategies and evaluation frameworks for GenAI and agentic systems. Build Python-based tests across APIs, UIs, performance, prompts, and regression scenarios. Measure model accuracy, relevance, safety, consistency, and latency; curate test datasets and golden examples; conduct adversarial testing for hallucinations and bias; maintain CI/CD test integration, production alerting support, documentation, and user acceptance testing.

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.

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.

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.

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.

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.