Principal Data Engineer role using GCP, Python, Node.js, BigQuery SQL, and Jenkins CI/CD. The team enables Sky’s growth through Data and AI, deepening customer relationships, optimising commercial performance, and enhancing operational efficiency. The role provides technical leadership, managing Solution Architects and setting engineering standards.

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.

You will build AI agents using Python, SQL, Docker, Kubernetes, AWS (RDS, EKS, S3), GitHub Actions, RAG pipelines, BM25, vector search, hybrid retrieval, and knowledge graphs. These agents interpret geopolitical and financial signals for Kharon, a company providing global security risk intelligence.

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.

Work with Java/Spring, React, JavaScript/TypeScript, CI/CD pipelines, and distributed systems. You'll join the Account Integrity team building a globally scalable fraud prevention engine to protect customer accounts and prevent financial crime using machine learning and real-time monitoring.

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.

Python, C++, KDB+/q, Git, Agile, CI/CD, and database technologies. You will partner with Quantitative Researchers to build scalable Python applications and high-performance data processing for research, analytics, and live trading at a hedge fund.

Wise★3.5 (42)· London

You will build and refine financial crime prevention systems using Java (or JVM languages) with Spring, React/JavaScript/TypeScript, and CI/CD pipelines, working 80% backend and 20% frontend. You join the Account Integrity team, which protects Wise’s global platform against fraud via machine learning and real-time transaction monitoring, and you will own projects end-to-end.

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.

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 will own the dbt project (models, tests, CI/CD) and work with BigQuery (or Snowflake/Redshift), advanced SQL, and a BI tool like Looker or Metabase. As an Analytics Engineer for a £100m+ D2C e-commerce brand, you'll transform raw data into trusted dashboards that drive marketing, finance, and operational decisions.

FDJ UNITED★4.0 (96)· London

You'll lead a DataOps/MLOps team building an ML platform and data operations capability for FDJ United's Sportsbook, working with AWS, Terraform/CloudFormation, Spark, Kafka, and CI/CD pipelines for end-to-end ML lifecycle management. The role involves establishing best practices, monitoring, and governance while collaborating with Data Engineering, ML Engineering, Quants, and Data Science teams to personalize customer experiences and manage risk.

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.

Design, build and optimise ETL data pipelines using Databricks, PySpark, Python, and SQL for a leading global investment bank. You will work on large-scale enterprise data platforms in a fast-paced financial services environment.

Develop core eTrading MI & Analytics platform components using q and kdb+, with CI/CD, TDD/BDD, and an additional programming language. The platform captures, stores, and analyses over 3TB/5 billion daily records from electronic trading venues for risk, quant, trading, and sales stakeholders.

You will work with Python, JavaScript, SQL, Kafka, AWS/Azure, PostgreSQL, SQL Server, ClickHouse, and MongoDB. The team designs and delivers real-time, high-performance trading systems for Energy & Commodities, integrating market data, risk, and front-to-back office workflows.

Work with Microsoft Azure data platforms: Azure Synapse, Azure SQL Server, Azure Data Factory, Azure DevOps, Microsoft Fabric, and OneLake; also uses DBT and Spark. You will modernize core data platforms—ingesting data from MS Dynamics CRM, Finance & Operations, and SAP SuccessFactors—to power analytics, AI-driven insights, and executive decision-making.

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.

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.

You'll use Python, relational databases, data pipelines, and optionally cloud (AWS/GCP), containers, CI/CD, and TDD. The team builds a scalable trading and backtesting platform for quant researchers and portfolio managers, handling market and alternative data for simulation and live execution.

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.

Build scalable distributed systems with Java (Spring/Spring Boot) and Python, using Kafka, MQ, XML, and CI/CD in Unix/Linux environments. This Risk Services team within CRISIL supports capital-markets risk data ingestion, processing, analytics, and reporting for Global Markets.