Lead 6-7 engineers on core ML infrastructure for a systematic quant fund: distributed model training, LLM hosting/fine-tuning, and scalable deployment. Use Python with working knowledge of C++/Java. Own technical direction for real-world, high-stakes systems.

Google★4.3 (6.2k)· London

Manage engineers building Google’s Learning Platforms within the Global Business Organization, which powers Google’s ecosystem of products and services for customers and partners. Work with back-end languages (Java, Python, Golang, C++) and front-end (JavaScript, TypeScript, HTML, CSS), plus ML model fine-tuning and evaluation.

This role develops generative AI applications using Java, Python, React or Angular, LangChain, LlamaIndex, Hugging Face, Docker, Kubernetes, and AWS, with tools like GitHub Copilot. You will build scalable, AI-powered solutions for Citi’s global finance operations.

Work with Nvidia GB200 72NVL CX8 and AMD MIxxx architectures, coding in C, C++, C#, Java, JavaScript, or Python to benchmark, profile, and debug GPU clusters. You will help build foundational models powering Microsoft’s personalized AI assistant, Copilot, spanning training and inference infrastructure, model architecture, and data curation.

bp★3.8 (5.2k)· London

You will work with Python, Java, Azure, LLMs, vector DBs, and RAG patterns, using tools like LangChain, Haystack, and Hugging Face. As part of bp's AI organization, you will design and deploy scalable, production-grade AI applications such as conversational bots and AI-powered search for the oil and energy sector.

You will work with Python, Java, Azure, LLMs, vector DBs, RAG, and frameworks like LangChain, Haystack, Hugging Face, and Crew AI. The AI organization builds and deploys scalable, production-grade AI-powered applications for business value within the oil and energy/HR technology sector.

bp★3.8 (5.2k)· London

Design, develop and maintain production-grade AI applications using Python, Java, microservices, AWS/Azure/GCP, CI/CD, LLMs, vector DBs, RAG, and tools like LangChain, Haystack, Hugging Face, Weaviate, and LlamaIndex. The team builds scalable AI-powered applications—including conversational bots, AI search, unstructured data processing, and generative BI—to solve high-priority business problems.

Citi★3.9 (21k)· London

Build and scale AI-powered financial systems using Java, Spring, React/Angular, Python, FastAPI, Docker, and GitHub Copilot within Citi’s global tech environment. You will design generative AI agents, LLM workflows, and RAG pipelines while working on enterprise microservices, mobile, or front-end development. This role is for an individual contributor on an agile team.

FDM Group★2.9 (270)· London

The role involves Python, Java, microservices, CI/CD, cloud (AWS/Azure/GCP), LLMs, vector DBs, RAG, AI agents, and frameworks like LangChain, Haystack, Hugging Face, Weaviate, LlamaIndex, Crew AI, AutoGen. The team builds scalable AI-powered applications (conversational bots, AI search, unstructured data processing, GenBI) for a client in the energy sector.

Accenture★3.9 (29k)· London

You will build production-grade agentic AI systems using LangGraph, CrewAI, AutoGen, OpenAI, Anthropic, Vertex AI, Kubernetes, Docker, Python, and Java. The role designs end-to-end multi-agent systems, RAG pipelines, and LLMOps (LangSmith, Braintrust) while embedding with client engineering teams to deliver enterprise solutions. No team size is stated.

Accenture★3.9 (29k)· London

You will design and deploy production-grade agentic AI systems using LangGraph, CrewAI, AutoGen, Python, OpenAI, Anthropic, Vertex AI, Kubernetes, Docker, Terraform, and LangSmith. The team builds multi-agent orchestrations, RAG pipelines, and LLMOps tooling for enterprise clients, embedding directly with client engineering teams to deliver scalable patterns.

Checkout.com★3.8 (15)· London

You will work with LLMs (GPT, BERT, Llama, Gemini), Python/JavaScript, AI frameworks (LangChain, PydanticAI), cloud platforms (AWS/GCP/Azure), Docker, Kubernetes, and Terraform. As part of Checkout.com’s AI Centre of Excellence, you will design, build, and deploy Generative AI solutions (chatbots, assistants, knowledge extraction tools) to enhance products and streamline business processes.

Work with Terraform, Pulumi, OpenFGA, Authzed, Cedar, Topaz, OAuth 2.0, OIDC, SAML, JWT, EntraID, Okta, AWS, and frameworks like Java, Python, Node.js, .NET to design and implement identity infrastructure for authentication and authorization. You will support the Identity Engineering team, which builds core systems for Scale AI’s products that power advanced LLMs and generative models through RLHF, human data generation, and model evaluation.

You will work with CUDA and Triton kernels, Python, Kubernetes, and AWS to build systems that train and serve large transformer and LLM models. The team creates and scales training pipelines and inference services for Fin’s AI Customer Agent, which resolves customer service queries across channels.

Wise★3.5 (42)· London

You'll build ML platform infrastructure using Python, Kotlin/Java, SQL, Airflow, Kubeflow, MLflow, and container-based deployment. Your team owns the model lifecycle platform for financial crime detection – designing training pipelines, model serving, evaluation, and monitoring systems from the ground up. This is a greenfield build with strong senior leadership investment.

Work with Python, SQL, and ML stacks (PyTorch, TRL) to build evaluation and data pipelines for LLMs and AI coding agents. The team owns how AI agents generate, analyze, and improve Kotlin code across Android, KMP, and server platforms, creating benchmarks and post-training pipelines.

Accenture★3.9 (29k)· London

You will build production agentic systems using LangGraph, CrewAI, or AutoGen, with RAG pipelines, multiple LLM providers (OpenAI, Anthropic, Vertex AI), and LLMOps tooling like LangSmith. You design and deploy multi-agent orchestration, eval harnesses, and observability systems directly with client engineering teams at Accenture.

Accenture★3.9 (29k)· London

You will design and deploy production-grade agentic AI systems using LangGraph, CrewAI, or AutoGen; build RAG pipelines with vector databases; integrate OpenAI, Anthropic, and Vertex AI APIs; and use LangSmith, Kubernetes, Docker, Terraform, Python, and Java. You work on client-embedded teams building production multi-agent architectures, eval harnesses, and reusable accelerators. The role is within Accenture, a global professional services firm.

You will work with Python, TypeScript/JavaScript, Java, or C++ and cloud platforms (AWS, GCP, Azure) to build and deploy end-to-end generative and agentic AI applications, create training datasets, and design evaluation pipelines for LLMs. The Global Public Sector team builds custom AI applications for government clients, impacting millions of citizens.

LexisNexis Risk Solutions★3.8 (231)· London

Build and deploy ML-powered services, tools, and full-stack applications for fraud and identity analytics using Python, Java, Snowflake, and ML-model-serving pipelines. Work on inference APIs, microservices, feature pipelines, and DevOps for real-time production systems.

RELX Group★3.8 (133)· London

You will build and deploy ML-powered services, APIs, and microservices for fraud and identity analytics at LexisNexis Risk Solutions, working with Python, Java, Snowflake, and ML model-serving pipelines across backend systems, feature engineering, and full-stack applications. The role requires 4+ years of software engineering experience with strong ownership of DevOps and secure engineering practices, and involves collaborating with data scientists to integrate models into real-time production systems.

Universal Music Group★4.0 (408)· London

You'll work with Python, PyTorch, Hugging Face Transformers, LangChain, vector databases (Pinecone, FAISS, Milvus, Weaviate), cloud platforms (AWS, GCP, VertexAI, Bedrock, Sagemaker), Docker/Kubernetes, and LLMs/vision models to build AI/ML solutions for Universal Music Group's royalty calculations, A&R insights, marketing optimization, rights workflows, and creative tools across the music industry.

Intercom★3.5 (4)· London

You'll work with GPU optimization (CUDA, Triton), distributed training pipelines for transformers and LLMs, and inference services at scale—building the infrastructure powering Fin, an AI customer service agent resolving millions of queries monthly. The small, highly technical AI Infrastructure team focuses on training and serving custom models like Fin Apex, with responsibilities including kernel tuning, autoscaling, and collaborating with ML scientists to productionize cutting-edge methods. You'll mentor engineers and raise technical standards across Fin's AI platform.

Apple★4.1 (14k)· London

You'll work with large language models, reinforcement learning (RLHF, RLAIF, DPO, PPO), parameter-efficient fine-tuning (LoRA, QLoRA), and inference optimization across Python, Swift, and Java. The AI & Data Platforms team builds internal generative AI systems and customer-facing AI products at global scale, handling the full model lifecycle from pre-training through deployment. As a Principal ML Engineer, you'll own end-to-end systems, establish evaluation frameworks, mentor engineers, and shape the technical direction for generative AI adoption across Apple.

JPMorgan★3.9 (20k)· London

You will develop and deploy advanced machine learning models and generative AI applications using Python, TensorFlow, PyTorch, and AWS to solve operational challenges across JPMorgan Chase's International Consumer Bank. The role involves architecting AI/ML solutions for fraud prevention, identity services, payments, lending, and core banking products, with responsibility for ensuring scalability and security in production environments. You will mentor junior engineers and collaborate with senior stakeholders to translate business needs into technical solutions.