Full-stack Java development on Linux/Unix, using AI coding tools like Claude Code, Windsurf, Devin, or GitHub Copilot. The role is for Ascendion, a technology services company.

You will work with Python, AI coding agents (e.g., Claude Code, Codex), and potentially AWS, Kubernetes, and Temporal. Unitary builds Virtual Agents — AI-powered software workers that automate complex workflows end-to-end in customer tools, focusing on commercial insurance, marketplaces, and primary healthcare.

You will build and evolve customer-facing interfaces for AI Virtual Agents using React, TypeScript, Next.js and Vite, and refactor the frontend into a scalable platform with a shared component library and design system. Unitary’s factory builds custom operations agents that complete complex workflows end-to-end in customer tools.

Zoopla★3.9 (18)· London

A 12-month maternity cover role on a dual-stack C#, .NET, and TypeScript backend with React microfrontends, using AWS managed services (ECS/Fargate, Lambda, SQS, DynamoDB, DocumentDB) and Terraform/SST. You'll build B2B SaaS software for estate agents and home builders at Alto Software Group. Take end-to-end ownership of features from design to production, working with the product team and AI coding agents.

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

DeepL★3.8 (4)· London

You'll work with C# / .NET, RESTful APIs, distributed architectures, and optionally React/TypeScript, plus MCP, CLIs, OAuth, and AI coding tools. You join the API Enterprise team, which builds enterprise adoption features (usage insights, cost controls, RBAC, compliance, data residency) for DeepL's Language AI API platform.

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.

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.

Senior Software Engineer building a multimodal AI data fusion platform for Defence and Security using Python, distributed systems, container orchestration, and agent frameworks across cloud, edge, on-premise, and air-gapped environments. You'll design core platform components, develop AI orchestration systems, define deployment architectures, and influence technical direction across the organization. Requires 7+ years shipping production software, systems design expertise, and experience architecting multimodal data systems.

Work with Java, GitHub Copilot, Devin.AI, Citi Squad, Stylus Workspaces (Gemini, OpenAI, Claude, Llama), Docker, Kubernetes, and CI/CD. Build high-performance, real-time FX risk management systems for a greenfield platform with a 10+ year strategic commitment. As a VP, lead end-to-end delivery and technical direction.

You will work with AWS, Kubernetes, Terraform, Python, Java or Scala, and AI-driven development tools (Claude, LLMs). You will build and maintain the distributed-compute and data-science platform that powers Macquarie's trading and analytics workflows, collaborating with quantitative analysts and trading desks.

Use Python, Azure, Docker, Kubernetes, Apache Airflow, and GitHub Actions to build scalable ETL pipelines, financial data pipelines, and cloud-native infrastructure. This front-office role is at a systematic equity hedge fund modernising its quantitative research platform, working directly with portfolio managers and researchers. Experience with S&P Xpressfeed, Snowflake, Bloomberg, MSCI Barra, Databricks, and LLMs is advantageous.

You will configure and maintain Workday, Greenhouse, CultureAmp, Kyriba, FloQast, and Workato, using Claude Code and agentic AI tooling. As an associate cross-platform engineer on the Finance & People Technology team, you support the SaaS estate underpinning finance and HR at a global alternative investment management firm.

Rebuild data infrastructure for a market-neutral systematic equities fund using Python, Microsoft Azure, Docker, Azure Kubernetes, Apache Airflow, and GitHub Actions for CI/CD. Work on ETL pipelines, vendor integration, and cloud-based research platforms, with relevant experience in Snowflake, Bloomberg, MSCI Barra, or Databricks. The role spans data engineering and quantitative development to transform financial data into investment signals.

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

You will use Python daily, with Ruby useful but not required, and AI tools like Claude in your workflow. The team focuses on greenfield projects in a start-up or small business environment.

Capital on Tap★3.9 (55)· London

First and foremost a software engineering role, you will work with C#, .NET, agentic systems and orchestration layers, and AI enablement tooling. You will build "AI on Tap", the company's internal hub for AI agents, and integrate agentic testing/verification across the pipeline to accelerate delivery for this financial platform.

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.

You'll work across the full stack—backend distributed systems, cloud with IaC, and data pipelines including vector stores—using tools like Claude Code and AI agents for code review, refactoring, and parallel work. This Principal Engineer role on a large iGaming engagement oversees sportsbook, casino, and gaming platforms for a major client, managing a group of engineers and Tech Leads as the named delivery owner. The team includes engineers and Tech Leads that you directly line manage.

Koinly★3.0 (1)· London

Work with React 19, TypeScript, TanStack (Router, Query, Table), Radix UI, Mantine, Tailwind CSS v4, Vite, Vitest, Storybook, Highcharts, Sentry, Bun, and RabbitMQ on a data platform that reconciles crypto transactions and tax obligations for Koinly’s web application, with a React Native mobile app joining scope later.

Koinly★3.0 (1)· London

You will work with TypeScript on Bun, RabbitMQ, Redis, PostgreSQL, Vitest, Sentry, and Rust. The team owns the distributed data-processing backend that ingests, normalizes, and reconciles on-chain and exchange data for Koinly’s crypto tax calculation product.

You'll build scalable data infrastructure for AI applications using Python, SQL, Spark, dbt, LangChain, and cloud platforms (AWS, Azure, GCP), working with data pipelines, embeddings, vector search, and LLMOps tools like Terraform and LangSmith. QuantumBlack, McKinsey's AI division, develops next-generation AI systems and enterprise solutions for clients across industries. You'll collaborate with Data Scientists and ML Engineers in cross-functional Agile teams based in London, supporting the firm's AI adoption and complex problem-solving at scale.