Deloitte★3.9 (14k)· London

You will work with Python, NumPy, Pandas, scikit-learn, LangChain, Hugging Face, LlamaIndex, Azure Machine Learning, Foundry, Git, and Agile practices. The team is Deloitte’s Tax Digital Innovation AI & Data Studio, which researches, builds, and delivers analytics and AI-driven solutions for tax and legal clients.

Work with Python, SQL, JavaScript/TypeScript, NumPy, Pandas/Polars, visualization libraries, and deep learning frameworks (PyTorch, JAX, or TensorFlow) to design and deploy full-stack data-driven applications and machine learning solutions. You will be embedded with Arsenal's Men’s First Team Analysis department, building tools that directly support coaching and analysis workflows.

Work with Python, SQL, SHAP, Airflow, PyFlink, BentoML, Git, Jenkins, Octopus, Docker, JIRA and MLFlow. As a Data Scientist at Zempler Bank, you develop algorithms to protect customers, widen credit access and improve financial outcomes. You implement and monitor models in production.

Escalent★3.6 (38)· London

You’ll work with Python and frameworks like Django, Flask, or FastAPI, building RESTful APIs, microservices, and data pipelines while integrating AI/ML models and LLM APIs. Tech includes SQL/NoSQL databases, AWS/Azure, Docker, Kubernetes, and CI/CD. Escalent is a data analytics and advisory firm, and you’ll develop AI-powered applications and intelligent workflows in its cloud-native environment.

You will work with the PyData stack (pandas, numpy, scikit-learn, XGBoost) and advanced ML methods like neural networks to build predictive models and proprietary metrics from high-frequency cricket tracking data. CricViz is a market leader in cricket data analysis, serving broadcasters, teams and gaming platforms.

You will work with Python, NumPy, SciPy, simulation modelling, optimisation under uncertainty, cloud deployment, and machine learning. The team builds an AI operating system for secure environments, taking complex scientific models from prototype to production across government and commercial programmes.

You will work with kdb+, q, Python or Java, APIs (REST, messaging), Linux, AWS/GCP, Docker/Podman, and CI/CD. You will develop, test and maintain kdb+ applications and supporting services for market data and analytics on a Client Engineering team for a FinTech leader. You will also mentor junior developers.

Senior Software Developer role in the Data team of a global energy trading house, working on data-consumption and publishing platforms using C#, .NET, ASP.NET, Angular, SQL Server, Oracle, Docker, Grafana, Kafka, Azure Service Bus, and Azure DevOps. Responsible for end-to-end delivery, mentoring, and architectural direction.

Senior Machine Learning Engineer building production AI systems that help UK consumers find financial products. You'll use Python, MLflow, Kubernetes, Databricks, Airflow, LangChain/LangGraph, Terraform, and CI/CD pipelines to design and deploy batch and real-time ML models, LLM-powered capabilities, and monitoring tooling. You'll work closely with data scientists, engineers, and product teams to productionise experimental models and evolve the internal ML platform.

You will work with Python (pandas, NumPy, SciPy, matplotlib/seaborn), SQL, cloud data tools (ideally Azure), and large language models (LLMs) to build predictive models and identify alpha signals. You will join the data science team of a leading investment management company.

Wise★3.5 (42)· London

You will build scalable systems for business onboarding and verification using Java or JVM languages, Spring framework, JDBC, and relational/non-relational databases with CI/CD pipelines and distributed systems. This engineering team owns end-to-end projects to automate verification, create flexible onboarding journeys, and manage compliance risks for Wise’s money-moving platform across Europe and the UK.

Lead ML engineering using Python, PyTorch, TensorFlow, MLOps (CI/CD, experiment tracking, model registries) to build computer-vision and generative-AI systems. The role delivers live-sports insights—player performance, contextual statistics, injury risk—and integrates them into personalisation engines, with mentoring of Senior and Mid-Level Data Scientists.

Chubb Insurance★3.7 (1.3k)· London

You will use Python, Hugging Face Transformers, LangChain, LlamaIndex, PyTorch, TensorFlow, Docker, and cloud platforms to build and deploy LLM, RAG, and conversational AI applications. As part of Chubb Insurance's Global Analytics team, you'll design production-ready AI solutions for business value.

You'll work with Java/JVM and Spring on the backend, React/JavaScript/TypeScript on the frontend, plus CI/CD, distributed systems, and relational/non-relational databases. You'll join Wise's Account Integrity team, building fraud-prevention and financial-crime detection systems to protect customer accounts. Wise is a global technology company for moving and managing money.

Tesco★3.5 (25k)· London

You will work with Java, Spring Boot, Kafka, Apache Flink, Apache Beam, NoSQL databases, Redis, and Apache Camel. The Tesco Personalisation team builds real-time, low-latency services for recommendations, offers, and experimentation across web, mobile, and in-store channels using streaming data and ML.

You will work with PyTorch, TensorFlow, JAX, CNNs, Vision Transformers, and Python-based tools to evaluate and improve AI models for image recognition, object detection, segmentation, and visual reasoning. This contract role involves assessing model outputs, identifying failure modes, and refining training data for diverse visual domains.

Senior Python Engineer building trading, risk, and analytics applications using Python, Pandas, NumPy, FastAPI/Flask, SQL, and Linux/Unix. The role designs and develops scalable systems for risk analysis, trade processing, and data services, integrating with pricing and risk platforms for a financial services organisation.

This senior software engineer role uses Python, Snowflake, dbt, Kafka, Java, AWS, and Linux/Docker. You will build ELT pipelines and a streaming platform for tick data, consolidating legacy systems as part of the data engineering team for a systematic hedge fund.

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.

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.

You'll use Java, Kafka, Flink, SingleStore, HDFS/S3, and Scala to build high-performance, low-latency data systems. The Data Platform team in Global Markets owns the post-trade stack, processing high volumes of data and developing an AI-powered analytics UI/agent framework.

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.

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.