Top AI Engineer Jobs in London

Own the technical design, delivery, evaluation, and reliability of multi-component AI systems. Build data pipelines, evaluation frameworks, model-tool boundaries, feedback loops, and internal development harnesses. Ship LLM-backed products, optimize latency and cost, assess external models and datasets, and support APIs, SDKs, and agent-oriented applications. Participate in on-call rotation, conduct code and design reviews, and mentor engineers on evaluation practices.

Design, build, and deploy AI-powered products using frontier LLMs. Create agents, RAG systems, and backend services; evaluate and improve model quality, ensure trust/privacy, and collaborate with product, design, and finance teams.

Own AI cost visibility features end to end, from Go backend services and APIs processing cloud spend data to TypeScript and React interfaces. Define product direction, understand FinOps and AI economics, build scalable cloud software, and use AI throughout development. Lead design reviews, establish engineering standards, mentor peers, and ensure reliability, usability, and measurable customer impact.

Lead discovery, rapid prototyping, and deployment of production-ready AI solutions for supply chain and manufacturing. Build Python-based RAG pipelines, semantic search, multi-agent workflows, data integrations, and enterprise platform connections. Engage frontline operations, translate business needs into technical requirements, manage agile delivery, and ensure prototypes are scalable and transferable to core engineering teams. Work across SAP, MES, shop-floor platforms, databases, telemetry, and unstructured data while collaborating with security, data, and architecture teams.

Develop high-performance C++ SDKs that expose computer vision capabilities, deploy and optimize machine learning models across Qualcomm, Intel, and NVIDIA hardware, benchmark model performance, and build Python automation and CI/CD pipelines for testing and deployment. The role requires expertise in modern C++, Python, model deployment frameworks, and Docker, with bonus experience in deep learning, model conversion, and quantization.

Build and maintain scalable Python libraries, tools, and infrastructure enabling machine learning teams to train, evaluate, and deploy models at scale. Develop abstractions for distributed training, data loading, inference, checkpointing, and evaluation; support large GPU clusters and cloud environments; optimize multimodal data pipelines; and improve reliability through architecture, testing, observability, documentation, and maintainability.

Build, deliver, and operate AI-focused DevOps environments across hybrid infrastructure, Azure, and AWS. Develop RAG and data pipelines, MLOps workflows, AI agents, GPU workloads, infrastructure automation, and containerized platforms. Support live, business-critical systems while improving scalability, reliability, observability, and standardization. Collaborate across teams, modernize infrastructure, and implement emerging AI and cloud technologies.

Build and refine AI-powered prototypes for supply chain and manufacturing operations. Engage with factory and warehouse teams, translate operational needs into software requirements, develop Python-based RAG and agentic systems, integrate APIs and data sources, and iterate prototypes based on frontline feedback. The role involves SQL and document data preparation, agile collaboration, occasional manufacturing-site travel, and progressive adoption of enterprise AI frameworks and production engineering practices.

Design and build scalable Python microservices and APIs for an AI-powered code modernization and migration platform. Develop MCP servers and agent tools, orchestrate agentic and multi-agent workflows, and manage backend service lifecycles. Ensure services are robust, observable, tested, and production-ready while shaping tool-calling, state-management, and distributed-system architecture. The role also involves Git workflow automation, developer tooling, and potentially containerized deployments and agent-facing interfaces.

Design, build, and operate internal developer platforms and production-ready infrastructure. Manage Kubernetes clusters, IaC (Terraform/Pulumi/CDK), CI/CD pipelines, secrets management (Vault), and SRE/security practices. Support AI/ML platform needs (GPU scheduling, model serving, MLOps, vector DBs) and lead client-facing technical discovery, documentation, and coaching.

Build and maintain React front-end components and responsive UIs for a control plane; orchestrate agentic harnesses (Claude) to generate, validate, and test code; contribute to Python/FastAPI back-end APIs; create templated archetypes and golden-path tooling; collaborate with UX; ensure performance, security, and testing-first maintainability.

Design, build, and operate production AI systems and agents for private equity workflows. Own agent harness, tool integrations, retrieval/search, evals, and document ingestion pipelines. Ship and iterate features end-to-end to meet professional reliability and citation standards, working across backend and ML infrastructure with product and engineering teams.

Design, build, and deploy intelligent AI agents for e-commerce and logistics. Develop agent architectures using ML, NLP, reinforcement learning, and multi-agent techniques; integrate with JD systems; run experiments, monitor performance, and collaborate with cross-functional teams to deliver scalable, production-ready solutions.

Lead development of production AI behavior, governance, and MLOps standards for high-risk public-sector systems. Architect and validate safety-focused AI components, build reusable agent and evaluation capabilities, advise executive clients on AI strategy, resolve severe model-safety incidents, and coach senior engineers while shaping the technical roadmap and recruitment.

Develop and deploy AI systems using generative AI frameworks, LLMs, RAG pipelines, knowledge graphs, vector stores, and agentic architectures. Integrate models with APIs, data sources, and production systems while applying cloud-native, DevOps, prompt engineering, evaluation, observability, and LLMOps practices. Collaborate with clients and cross-functional teams in an agile consulting environment, prototype emerging technologies, and share knowledge across the AI community.

As an ABAP Developer, you will leverage your SAP expertise to enhance AI frameworks, contribute to full-stack development, and mentor peers in SAP contexts at Conduct.

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.

Build and embed AI/LLM capabilities into FP&A solution assets: design prompts, AI workflows and prototypes; implement features for planning, forecasting, reporting and insights; validate AI outputs; document prompting logic and enablement materials; collaborate with product, architecture, and FP&A experts.

Designs and implements AI-driven solutions across the software development life cycle using the GitHub ecosystem. Responsibilities include architecting and tuning coding agents, building evaluation frameworks, governing prompt libraries, integrating AI with CI/CD pipelines and MCP servers, and embedding agentic workflows into enterprise engineering processes. The role requires advanced programming expertise, strong AI architecture knowledge, and experience applying generative AI within complex enterprise SDLC environments.

Design and implement AI infrastructure for cross-border e-commerce: optimize GPU scheduling on Kubernetes or Ray, improve cluster high-availability and fault tolerance, build efficiency evaluation models, and introduce cutting-edge AI infra technologies.

Design, build, and maintain high-performance Java backend systems that integrate and deploy AI/ML models. Develop RESTful microservices, optimize performance and security, implement CI/CD, work with cloud platforms and container orchestration, and collaborate with data scientists on scalable ML solutions.

Lead the development and optimization of AI and machine learning models, mentor junior engineers, and enhance AI-driven features for the platform.

The AI Engineer designs and deploys production-grade AI systems using LLMs, collaborates with clients to understand complex problems, and improves AI application workflows and architectures.

The Founding Product Engineer will develop AI-integrated platforms for healthcare, improving administrative workflows and enhancing the patient-clinician experience within the NHS.

As an AI Inference Engineer, you will optimize C++ systems for AI model inference on edge devices, collaborating with researchers to deploy and enhance models while ensuring runtime stability and performance.