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.

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 work with Python, PowerShell, SQL, OMS/EMS platforms (FlexTrade, Triton, SpiderRock), Linux, Windows, cloud (AWS/Kubernetes), FIX, messaging systems, market data flows, and monitoring tools like Geneos/Zabbix. As part of a global hedge fund’s front-office technology team, you will directly support equity portfolio managers, traders, and analysts to ensure the reliability and performance of business-critical trading systems.

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.

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.

You'll build distributed backend services and microservices using Java (Spring/Spring Boot), Python, messaging frameworks like Kafka/MQ, and Unix/Linux, with CI/CD and containerization. The Role sits on the Risk Services team within Global Markets, designing scalable systems for risk data processing, analytics, and reporting workflows.

Build and maintain scalable data pipelines (BigQuery, Cloud Composer, Airflow, Dataflow, Spark, Flink, dbt) on GCP for AI/ML experiences. Use Docker/k8s, Terraform, SQL, shell scripting, and monitoring tools (Monte Carlo, Looker, NewRelic, Splunk). Collaborate with product engineering, data science, and business teams.

AXA★3.9 (2.9k)· London

Design and implement secure architectures for enterprise security and AI/ML initiatives across AXA, using Docker, Kubernetes, cloud security, API security, micro-services, MLOps/MLSecOps platforms, threat modeling, and SSDLC. Defines technical security standards and reference architectures aligning with frameworks like NIST AI RMF and ISO/IEC 42001. Reports to the Chief Security Officer.

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.

JPMorgan★3.9 (20k)· London

You will work with Python, AWS, EKS, Docker, Kubernetes, Kafka, and MLOps tools. As a member of the Applied AI/ML Software Engineering team within CIB Security Services Technology, you will build cloud-based frameworks for hosting machine learning models alongside Data Scientists to integrate AIML solutions into production systems.

Capgemini★3.7 (11k)· London

Design, build, and manage data pipelines and models using AWS Snowflake, Azure Data Lake, Databricks, Redshift, and BigQuery. Automate CI/CD with Azure DevOps, GitHub Actions, Jenkins, GitLab CI/CD, and provision infrastructure with AWS CloudFormation, Docker, ECS, EKS, and Kubernetes. Capgemini is an AI-powered global business and technology transformation partner.

EXL Service★3.7 (2k)· London

You will work with Python, FastAPI, LLM frameworks (prompt engineering, RAG, agentic systems), and cloud AI platforms (Azure OpenAI/AWS Bedrock/Claude) on Kubernetes. As a Sr. AI Architect & Client Partner at EXL, a data and AI services company, you will design and deliver production-grade Generative and Agentic AI systems for enterprise clients, converting ambiguous mandates into secure, scalable solutions while shaping deals and reusable IP.

Quantiphi★3.3 (60)· London

As an AI Engineer at Quantiphi, you will work with the GCP stack (AlloyDB, GCS, GKE, Gemini Enterprise, Agent Platform, BigQuery, Cloud Run, Vertex AI Pipelines), advanced Python, and tools for agentic AI, MLOps (Kubeflow, Docker, Kubernetes), and CI/CD. The company is an Applied AI and Big Data services firm delivering industry solutions across Healthcare, Financial Services, and more, with a focus on Generative AI and Agentic AI.

Bloomberg★3.8 (1.1k)· London

This senior full-stack role involves building Bloomberg’s DataHub platform using React for the UI and Java, Kotlin, Scala, or Go for backend services. You will design control-plane systems enabling engineers to discover, query, publish, and manage datasets at scale, while also building the semantic foundation for AI-assisted data workflows.

You will lead a small cross-functional data pod building data pipelines, models and services in Python, SQL and Snowflake to turn supply chain data into customer-facing sustainability insights. The role combines hands-on data engineering with technical leadership and line management of the pod's engineers.

This role develops generative AI applications using Java, Python, React or Angular, LangChain, LlamaIndex, Hugging Face, Docker, Kubernetes, and AWS, with tools like GitHub Copilot. You will build scalable, AI-powered solutions for Citi’s global finance operations.

Work with Quantexa, Spark, Scala, Python, Databricks, DevOps, CI/CD pipelines (Jenkins, GitHub Actions, Azure DevOps), Docker, and Kubernetes. Lead data engineering for a global technology innovator, building and managing data solutions.

Senior Software Engineer building and operating systems that move and safeguard cryptoassets—treasury automation, wallet inventory, custody integrations, and operational controls. Work with Python (FastAPI), Kubernetes, CI/CD, GitOps, relational data modeling, and job orchestration tools (Airflow, Prefect).

Work with Python, React, Node.js, AWS, Docker, Kubernetes, CI/CD, APIs, PostgreSQL, and AI technologies including LLMs, RAG, and agentic workflows. You'll build production-grade applications and AI-powered workflows for a global commodities trading firm, working directly with front-office users on bespoke integrations from prototype to production.

Build and evolve ASOS's data and ML platform using Python, Spark, Azure Databricks, ADLS Gen2, Unity Catalog, Terraform, and CI/CD tools. You'll implement IaC, manage data/ML lifecycle services (pipelines, experiment tracking, versioning), and support a centralised AgentOps capability with LLM gateways and safety controls. This team enables consistent, reproducible data engineering and ML workflows across the company.

You will design, build, and operate AWS infrastructure, engineer Kubernetes environments (EKS, Docker, Helm), and use Terraform for infrastructure-as-code. You will support critical investment and trading systems, improve CI/CD pipelines with GitHub Actions and GitOps (Argo CD), and work with databases like Snowflake, Aurora, and SQL Server. You will take end-to-end ownership of production systems, solve complex problems across distributed environments, and influence cloud architecture and reliability standards.

Fivetran★3.3 (8)· London

Senior Sales Engineer working with AWS, GCP, Azure, Snowflake, BigQuery, Databricks, Kubernetes, Docker, Podman, Grafana, Linux, Windows, Python, Java, and databases like Oracle, SQL Server, Postgres, MySQL, and NoSQL. The role supports Fivetran’s Commercial sales team by understanding customer data replication needs and leading proof-of-value processes for the company’s automated data pipeline and transformation solutions.

You will design and build data pipelines, models, and services in Python and SQL, using Snowflake for warehousing. You will lead a small cross-functional data pod that transforms supply chain data into customer-facing insights for Sedex’s SaaS sustainability platform. This role includes line management of the pod’s engineers.