Work with Microsoft Fabric (Lakehouses, Warehouses, Dataflows, Pipelines), Azure (SQL, Data Lake, Data Factory), SQL, Python, and REST API/SFTP to build ETL/ELT solutions. You will deliver modern data integration solutions for a diverse portfolio of clients within RSM’s Global VAT Team.

MAREX★3.2 (6)· London

Risk Data Analyst at Marex works with the Data Platform, Power BI, and structured/unstructured datasets. The role bridges Risk and Technology, supporting data-led risk management, monitoring, and reporting across disciplines like market, credit, and operational risk.

Lead a team of Data Scientists and ML Engineers building AI solutions for financial markets using LLMs, Generative AI, Deep Learning, Transformers, Agentic AI, Python, PyTorch, TensorFlow, Hugging Face, LangChain, Semantic Kernel, Azure AI Foundry, AWS AI Services, and MLOps/LLMOps practices.

Build backend systems with Kotlin or Java, Kafka, PostgreSQL, Redis, and microservices for a crypto savings and crypto-backed lending platform. This hands-on technical lead defines architecture, remains close to the code, and leads a small team of engineers.

Work with Python, LLMs, RAG pipelines, vector databases, Hugging Face Transformers, LangChain, PyTorch, and Docker to build production AI systems. Design, develop, and deploy AI-powered solutions such as conversational AI, document intelligence, and backend services for the Global Analytics team.

Lead a team of DataOps and MLOps engineers building and evolving the ML platform and data operations for FDJ United’s Sportsbook product. You’ll work with AWS, Terraform, CloudFormation, Spark, Kafka, and CI/CD pipelines to enable end-to-end machine learning workflows from research to production.

Develop backend services using Node.js, Python, Java, C#, PHP, and Ruby on Rails, with databases including MySQL,

TypeScript, React, Node.js, Bun, Kubernetes, and AWS. The team builds web apps, back-office systems, and tooling for financial product comparison across money.co.uk, Uswitch, and Confused. As a Staff Software Engineer, you set technical direction for Financial Services.

You'll use Python, SQL, shell scripting, containerization, infrastructure-as-code tools, and Scala/Java or Python to build and maintain scalable data pipelines. You'll support a financial technology company's data infrastructure, contributing to AI/ML experiences and international applications. No team size is stated.

Design, build, and operate a Kubernetes-based MLOps platform using Helm, Terraform, Kubeflow, KServe, vLLM, and JupyterHub. The platform supports the full model lifecycle—experimentation, deployment, and scalable inference—for AI, ML, and LLM workloads in production.

You'll build and operate a Kubernetes-based AI/ML platform using Helm, Terraform, Kubeflow, KServe, vLLM, and JupyterHub. This role focuses on creating infrastructure, tooling, and guardrails so MLOps and data science teams can reliably deploy and serve models at scale.

You will work with AWS, Python, SQL, ETL/ELT pipelines, data warehousing, and APIs. You will own the data platform for a company developing AI and machine learning products that help enterprise clients make decisions. You will start as a senior individual contributor with the opportunity to build and lead a future data team.

Senior Developer role working with C#, .NET 4 & 6, SQL Server, Azure cloud services, SignalR, RabbitMQ, Kafka, Azure Service Bus, and CI/CD via Azure DevOps in an Agile environment. The team delivers a complex SaaS product for a regulated financial marketplace, requiring experience in financial or trading software.

Develop C# .NET BI applications using Visual C#.NET, Oracle, and SQL Server. Support existing applications and environments for a Japanese banking group's BI IT department, including data analysis with T-SQL and PL/SQL. Troubleshoot systems on Windows Servers and databases, liaising with teams in London and New York.

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 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.

Lead Software Engineers at ARQ work with Java 25, Spring Boot, Postgres, TDD, DDD, and CI/CD to build REST APIs and microservices. The team develops fintech systems powering card payments, treasury management, transaction booking, and crypto liquidity.

Work with Java 25, Spring Boot, Postgres, and CI/CD practices building REST APIs and microservices. The team develops backend systems for fintech products including card payments, treasury management, and crypto liquidity.

Lovable★4.0 (43)· London

Uses Go, Rust, TypeScript; AWS, Azure, Google Cloud, Cloudflare; Kubernetes, Terraform, Temporal, OTEL, CI/CD; PostgreSQL, ClickHouse, Firestore, Spanner, BigQuery. Builds the platform for Lovable Enterprise enabling secure, cloud-agnostic deployment, defining architecture for sandboxing, networking, encryption, identity, and mentoring engineers.

Senior Software Engineer will work with Java 25, Spring Boot, PostgreSQL, REST APIs, and microservices, using TDD, DDD, and CI/CD. They will build and maintain backend systems for a fintech focused on cross-border money movement, including card payments, treasury, transactions, and crypto liquidity.

You'll work primarily with TypeScript, Node.js, and PostgreSQL on GCP, designing and building scalable backend systems. The team develops and maintains a robust investment platform that powers features like mutual funds, bonds, and family investment tools.

Backend engineers use Python, Django, Django-REST-framework, GraphQL (Graphene), Celery, Postgres, and AWS (Packer, Consul, Terraform). They build and maintain the AI-driven energy management platform that powers utilities and optimises renewable generation. The global engineering team is around 1,400 people.

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.

Use deep learning frameworks, distributed computing, GPU-based training, and MLOps to build and deploy batch and real-time ML models. The Search & Recommendations team develops personalised recommendations, product ranking, and AI styling experiences for ASOS's online fashion platform, helping customers discover products.

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.