Monzo★3.5 (108)· London

You will work with LLMs, autonomous agents, RAG, sequence-based architectures, Python, SQL, and Go, developing and deploying machine learning models in production. You’ll lead the design of a human-in-the-loop system that predicts, identifies, and routes customer problems to scale support for Monzo’s banking products across multiple geographies.

You will use TensorFlow or PyTorch, Python, and CV/graphics libraries, plus 3D engines like Unreal or Unity, to develop generative models for graphics and video. As part of the Visual Computing Group, you'll design neural networks and ML for PlayStation's game rendering and live

Senior Machine Learning Engineer at ASOS, building production ML systems for personalized outfit recommendations and style discovery. You'll work with deep learning frameworks like PyTorch/TensorFlow, distributed GPU infrastructure, and MLOps practices including CI/CD, containerization, and observability. Collaborate with ML Scientists on batch and real-time models serving millions of customers.

Native Kotlin and Jetpack Compose development on a consumer-facing Android app for in-store grocery trips, running on a known device inside live stores. Integrates on-device edge AI (ML Kit, TensorFlow Lite) and uses AI coding tools like Cursor or Claude Code. Reports to the Head of Android.

You'll work with PyTorch, TensorFlow, XGBoost, GPUs, CI/CD and containerisation on the Search & Recommendations team, building production-grade ML systems for personalised recommendations, product ranking and AI-driven styling at ASOS, covering the full lifecycle from experimentation to deployment.

You'll design and own end-to-end RAG pipelines for payments domain content using Python, Azure Data Factory, Functions, ADLS, CosmosDB, and AI Search, with CI/CD via GitHub Actions and Azure DevOps. RedCompass Labs builds AI-powered payment transformation and financial crime detection solutions, including the RedFlag Accelerator for identifying human trafficking. You'll collaborate with AI engineers and payments experts to turn regulatory documents and handbooks into a reliable retrieval layer for their proprietary multi-agent AI platform.

Wipro★3.8 (17k)· London

You will work with GenAI, LLMs, RAG, agentic frameworks, cloud platforms, DevOps, MLOps, and data governance. As an AI Strategic Advisor, you will define enterprise AI strategy, lead integration of generative and agentic AI, and architect full lifecycle AI initiatives for a global technology consulting firm.

A Senior AI Engineer at Stakemate will use agent frameworks (LangGraph, LlamaIndex, Semantic Kernel, ADK), RAG, Python, cloud services (Azure/GCP/AWS), and SQL to move AI from experiments to production. They will shape the platform and architecture for a live betting product at a fast-growing social sports betting startup.

You will work with agent frameworks (LangGraph, LlamaIndex, Semantic Kernel, ADK), RAG pipelines, vector stores, Python, SQL, and a major cloud (Azure AI Foundry, GCP, or AWS). You will take AI agents from proof-of-concept to production within Stakemate’s live sports betting product, setting the governance and standards for the company’s AI estate. Reporting to the CTO, you will shape the platform and architecture as the company scales.

Python, PyTorch, JAX, LLMs, vLLM, vector databases and modern agent frameworks will be used. The stealth AI startup is building a pre-launch, next-generation AI assistant for everyday tasks, conversations and workflows — a 0-to-1 opportunity on a small engineering team.

You will work with Python, LLMs, agentic workflows, retrieval-augmented generation (RAG), and internal AI platforms. The Tactical AI Automations team deploys and supports AI tools across PIMCO's Portfolio Management and Analytics groups, including operational support for Lane Assist and POET (portfolio optimization tools).

Machine learning engineers at this tech-centric prop trading firm will work with Python tools and libraries, plus ML frameworks like JAX or TensorFlow, building training and inference infrastructure. The role focuses on enhancing research workflows and designing APIs for the growing ML team, applying techniques such as neural networks, random forests, and ensemble methods.

Use Python, PyTorch, TensorFlow, scikit-learn, LLMs, NLP, and SQL to build and deploy ML models for trade surveillance. You'll enhance analytics that detect market manipulation by analyzing trader behavior and order execution. Collaborate

Design, build and deploy production Generative AI applications, LLM-powered solutions and agentic frameworks using Python, LangChain, LangGraph, and OpenShift. You will automate enterprise workflows with intelligent agents, working with PostgreSQL and pgvector as part of Citi’s COO-Technology team building scalable AI for global operations. This is an Individual Contributor Staff Engineer role.

You will work with Python, PyTorch, TensorFlow, and causal inference tools (DoWhy, EconML, CausalML) to build production AI pipelines, APIs, and microservices. You will design and deploy behavioural, sequential, and causal AI models into Digital Twins, agentic decision engines, and intelligent automation. No team size is specified.

HSBC★4.0 (16k)· London

Work with Python, Docker, Kubernetes, Flask/FastAPI, PyTorch/TensorFlow, SQL Server/Postgres, and CI/CD pipelines to build and deploy production-grade ML and GenAI services. You will join the Growth AI programme within HSBC’s Corporate & Institutional Banking Data & Analytics, developing AI applications to identify business opportunities and mitigate client attrition.

Moody's★3.6 (589)· London

You will work with Python, TypeScript, Java, or Go; cloud platforms (AWS, GCP, Azure); Docker, Kubernetes, PostgreSQL, MongoDB, Redis, vector databases; and build AI applications using LLMs, RAG, prompt engineering, and AI agents. The Innovation team builds next-generation internal and external products powered by large language models, AI agents, machine learning, and natural language processing.

EXL Service★3.7 (2k)· London

Design AI architectures on Azure using .NET, React, LLMs, RAG, OCR, and agentic frameworks (LangGraph, AutoGen, CrewAI) to build production-grade GenAI platforms. The role focuses on processing complex insurance documents for underwriting, claims, and reinsurance use cases. You will lead and mentor a team of .NET, React, and GenAI developers.

Edited★2.7 (3)· London

You will work with Python, *nix, LLMs, MCP, Django, Flask, FastAPI, Elasticsearch, and SQL databases. You will build an AI chatbot with agents and tooling, and scale a large-scale retail data infrastructure on AWS and bare metal for the company's AI-driven retail intelligence platform.

Pipedrive★4.2 (5)· London

Lead a growing team of Applied Scientists and AI/ML Engineers at Pipedrive, a cloud-based CRM platform serving over 100,000 companies. You'll own the technical strategy for AI/ML solutions working with structured CRM data and unstructured content, designing autonomous agentic workflows using LangGraph, RAG architectures, vector databases, and LLMOps infrastructure. The role requires 8+ years of applied ML/AI experience in production environments, deep expertise in modern AI engineering, cloud infrastructure (AWS/GCP), and distributed systems, with proven ability to lead and scale technical teams.

You will work with Python, PyTorch, JAX, and TensorFlow to develop optimisation, control, and machine learning methods including reinforcement learning and physics-informed ML. The role is in UCL Computer Science's Multi-Sensory Devices Group, whose research covers wearable sensing, robotics, and biomedical devices.

Design AI architectures on Azure using .NET, React, LLMs, Azure OpenAI, RAG, Agentic AI (LangGraph, AutoGen, CrewAI), OCR, microservices, and Azure AI Search. Build scalable GenAI platforms for intelligent automation in insurance functions like underwriting, claims, and reinsurance. Lead and mentor .NET, React, and GenAI developers.

Senior WordPress Developer role: build bespoke WordPress sites and apps using PHP, MySQL, ACF, WooCommerce, Gutenberg, JavaScript (ES6+), SASS/SCSS, Git, Composer, npm, Vite, CI/CD. You’ll also drive AI-assisted development (RAG, MCP, Copilot) and mentor junior developers.

You will work with React/Next.js/TypeScript and Python on an agentic AI platform that ingests sustainability data, runs scenarios and recommends actions. The company is building a workflow and decision-making layer for sustainability, using AI agents and a knowledge context graph.

Waymo★3.0 (61)· London

You will use Python, deep learning frameworks (JAX, TensorFlow/PyTorch), and C++ (preferred) to develop data-selection algorithms and scaling laws for foundation model training. The AI Foundations team builds machine learning solutions for autonomous driving, focusing on reinforcement learning, generative modeling, and robust evaluation to improve the Waymo Driver. You will report to a Senior Staff Software Engineer.