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.

Python micro-services with RabbitMQ and OpenShift/Kubernetes, using SQL Server and Parquet. The London Commodities Risk Technology team builds a front-office distributed platform for trading and risk calculations on assets like oil, gas, power, and exotics, collaborating with traders and quants.

Java back-end developer using microservices, REST APIs, AWS (Lambda, EKS, RDS, S3, MSK), CI/CD, and AI tools (GitHub Copilot, LLMs). Builds next-gen trading and cash management systems for the Short-Term, Cash, and Collateral desks, modernizing platforms and migrating to AWS.

The role involves working with Java (server-side, multithreading), kdb+/q, Python, and CI/CD/testing frameworks. It sits within a specialist team that builds low-latency pricing, eTrading, and eRisk components for Rates and FX products, covering forwards, futures, swaps, bonds, and eCommerce quoting.

Java 11+, Spring Boot, microservices, event-driven architecture, Terraform, and cloud platforms (Azure, AWS, or GCP). The team builds post-trade technology solutions including business-critical applications, APIs, client reporting platforms, and web portals for capital markets. You will mentor developers and contribute to architectural decisions.

Java (JDK 11+), multithreading, SQL/NoSQL databases (Oracle, MySQL, PostgreSQL, MongoDB, Cassandra), message queuing (Kafka, Tibco, Solace), Linux, JVM, and TDD. This tech lead role builds high-performance FX risk calculation systems for Citi’s Markets technology team, which supports global trading and risk management.

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.

Work with Terraform, Ansible, Kubernetes, Python, Bash, Go, Jenkins, GitHub Actions, Prometheus, and Grafana to build infrastructure automation platforms supporting python-based quantitative research and trading systems. Manage on-premises resources across Ubuntu and Windows Server environments.

Builds cloud platforms for a systematic quant hedge fund's trading and research, using AWS (EC2, S3, VPC), Python/Bash, Jenkins/GitLab CI, Terraform/CloudFormation/AWS CDK, Docker, and Kubernetes to support digital asset and crypto 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.

You will work with Linux, AWS, Docker, Kubernetes, CI/CD pipelines, Python, Bash, and monitoring tools like Datadog, Prometheus, CloudWatch, or ELK. You will support and maintain the critical research and trading infrastructure for a quantitative hedge fund, acting as first-line support for platform-related issues.

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.

Build production-grade object-oriented Python applications and backend services for Fixed Income trading, including trade capture, execution workflows, and pricing distribution. You will work directly with traders and senior technologists, owning components and improving performance, threading, and concurrency in a front-office technology role.

Luxoft★4.5 (669)· London

Lead Python engineer designs, builds, and maintains risk engineering systems for a global commodity trading company, working on commodities risk and market data pipelines. Uses Python, AWS/GCP/Azure, CI/CD, containerization, and AI-assisted tooling. Collaborates with quants, traders, and risk managers in a high-velocity delivery environment.

Python, C++ and AWS with DevOps/CI/CD for real-time systems. You’ll build and enhance the quant tech stack for systematic crypto trading, integrating front-office systems and on-chain DeFi data feeds. You’ll also mentor junior developers.

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.

Use Python, Azure, Docker, Kubernetes, Apache Airflow, and GitHub Actions to modernize a systematic equity hedge fund’s quantitative research and engineering platform. You will build scalable ETL pipelines, cloud-native infrastructure, and research-ready datasets, working directly with portfolio managers and quantitative researchers.

Durlston Partners· London

You'll use Java, Spring Boot, Kafka, and AWS (EKS, S3), plus PostgreSQL, SQL Server, and CI/CD. The team builds the core trade processing, reconciliation, and reporting platform for a leading systematic hedge fund’s multi-asset trading, collaborating closely with traders and quants.

You will use low-level C++, automated testing, and CI/CD to design and develop low latency trading components and infrastructure for the Systematic technology team at Balyasny Asset Management L.P., a global asset manager.

Senior C# .NET Developer working on high-performance applications for Global Markets IT. You will use C#, .NET, MS SQL, and DevOps/CI/CD tooling to build event-driven architectures supporting XVA, Liquidity and Financial Resource Management, collaborating with Front Office stakeholders.

You will design and build Python services and APIs using AWS (Lambda, S3, ECS/EKS, Step Functions, Aurora), Kafka, and PostgreSQL/SQL Server/Snowflake to support portfolio construction, trading, and analytics. This Investment Technology role turns research tools into production-grade platforms for Portfolio Managers and Quantitative Researchers across Equities, Fixed Income, and Alternatives.

You will work with Java 11+, Spring/Spring Boot, Microservices, TDD/BDD (JUnit, Spock, Mockito), Git, Maven, and CI/CD on a cloud platform (AWS, Azure, or GCP). The role involves building and evolving a post-trade technology platform — including in-house systems, APIs, and client portal — for a specialist post-trade clearing and prime brokerage firm. You will also manage and mentor a small team of Java developers.

You will work with Playwright, JavaScript, TypeScript, automation frameworks and CI/CD pipelines on a strategic front‑office derivatives trading platform, embedded within the bank’s Front Office Derivatives Technology team.

Python, Microsoft Azure, Docker, Azure Kubernetes, Apache Airflow, GitHub Actions, Snowflake, Databricks, and OpenAI LLMs. The role rebuilds data infrastructure and transforms financial data into research-ready datasets and signals for a fully systematic market-neutral equity fund.

At Axon Labs you will own production infrastructure across multi-region cloud and latency-sensitive environments using AWS, Python, C++, Kubernetes, Terraform, CI/CD, PostgreSQL, TimescaleDB, Kafka, Redis, low-latency messaging, Grafana, Prometheus, and Loki. The company is a systematic trading and research lab that builds decision engines running in live markets, with multiple strategies already in production across venues.