Top Python Developer Jobs in London

Develop, manage, and sustain tools and services for software build, test, deployment, and release. Build and maintain API integrations and automation workflows, support Linux-based environments, and improve reliability while reducing operational toil. Mentor junior and graduate engineers and collaborate across engineering and security teams.

Build secure, scalable AI/ML platforms and applications to productionize research. Collaborate with AI researchers and data scientists, implement CI/CD and IaC, support experimentation environments, automate remediation, mentor junior engineers, and promote secure, responsible AI development practices.

Develop and maintain data pipelines and business applications to support model risk governance. Implement model performance monitoring, workflow automation, and LLM-enabled solutions using Python and web frameworks. Emphasize data controls, testing, AI-assisted development tooling, and collaboration with cross-functional stakeholders to deliver secure, reliable analytics and efficiency tools.

Design, build, test, and maintain scalable full-stack web applications and integrations using Java, Python, and React. Implement AI-enabled features, event-driven data pipelines, and marketing integrations (CDP/CRM/CMS). Improve code quality via AI-assisted tools, CI/CD, observability, and security/compliance best practices while collaborating with product, marketing, and partners.

Build and operate low-latency Python services for live Rates risk and P&L. Partner with traders and risk managers to deliver production-ready solutions, own releases/monitoring/incidents, troubleshoot distributed systems, and improve CI/CD and operational readiness.

Lead design and delivery of cloud-native data and AIML production systems. Build data engineering, backend, Cloud infra/DevOps and MLOps stacks, produce secure production code, architect microservices and distributed, data-intensive applications, and drive adoption/governance of AI-assisted engineering practices while communicating with stakeholders.

Lead design and delivery of high-performance, real-time data pipelines and analytics for trading. Drive AI-enabled data engineering, mentor engineers, manage global initiatives, and improve SDLC, testing, and operational reliability.

Lead design and deliver cloud-native, production-scale data and AI/ML engineering solutions. Build data engineering, backend, DevOps and MLOps stacks; produce secure production code and architecture artifacts; troubleshoot complex systems; collaborate with stakeholders and engineering communities to industrialize AI/ML models.

Design, build, test, and maintain scalable ELT data pipelines and architectures using Python and Databricks. Ensure data security, entitlement controls, and SDLC quality (CI/CD, testing). Use SQL/NoSQL, update data models, apply enterprise-authorized AI responsibly, and support controls and auditability across the data lifecycle.

Lead design, development, testing, release, and production support for Equity Derivatives workflows. Own production outcomes, incident triage, stakeholder communication, UAT, architecture and observability. Drive adoption of AI-assisted engineering practices, maintain release discipline, gather telemetry for stability metrics, and improve system reliability and coding hygiene through cross-functional collaboration.

Lead technical design and development of AI-native systems for credit pre-trade markets. Build scalable AI platforms, modernize legacy systems, deliver secure production code, partner with trading and product teams, support production operations, evaluate vendors, and drive engineering best practices and communities of practice.

Lead design and build developer-facing platform tooling (IDE experiences, web tools, test infra, SDLC workflows). Drive AI-assisted engineering adoption, implement static analysis, testing, CI/CD, cloud test execution, and governance to improve developer productivity and code quality.

Design, build, and maintain large-scale, secure data engineering solutions. Lead architecture, deliver production code, drive automation and operational stability, mentor teams, and evaluate vendor/technology choices to advance risk technology platforms.

Lead design and deliver IAM-focused public cloud platform services across AWS/Azure/GCP. Architect, implement, and operate secure, reliable production software; drive automation, CI/CD, and operational stability; lead design reviews, mentor engineers, and collaborate across platform and trust teams.

Lead integration and DevOps work for a low-latency equities onboarding team: participate in requirements, design, development, production rollouts, integration testing, release management, and automation using Linux, Python, shell scripting, networking and FPGA technologies to ensure stable, high-quality deliveries.

Lead development of the QES trading platform: design, build, test, and maintain production-grade software. Collaborate with front office (trading, structuring, research), deliver secure high-quality code (Python/Java/TypeScript/React), automate operational remediation, and lead agile engineering practices and communities of practice.

Design, develop, deploy, and maintain advanced AI products and data pipelines. Collaborate with engineers, data scientists, and product owners to deliver production-grade, secure, scalable code. Architect AI-enabled engineering workflows, improve SDLC automation, ensure compliance, and provide technical leadership and architecture governance.

Lead design and delivery of real-time marking, pricing, and market data platforms for Rates Nonlinear. Build secure, high-quality production code, drive AI-assisted engineering adoption, collaborate with front office and quant teams, and lead cross-functional initiatives to ensure resilient, scalable solutions across the trade lifecycle.

Lead development of engineering productivity and operational tooling for a large front-to-back platform. Build event-driven monitoring, runtime configuration, and AI-enhanced features; own full lifecycle from discovery to production support; partner with platform teams, Controls, and Audit to deliver safe, scalable, and widely adopted capabilities.

Lead development of microservices that fetch, enrich, consolidate, and distribute network metadata. Design, build, test, and maintain secure, resilient production code; drive adoption of AI-assisted engineering practices; automate remediation and CI/CD; evaluate vendor solutions; coach teams on responsible AI and cloud/network technologies; lead communities of practice and ensure operational stability.

Lead development and enhancement of equity derivatives trading and analytics applications, drive automation and AI-assisted engineering practices, collaborate with trading and quant teams, maintain global platform stability, and lead the Systematic Quoting Platform workstream for EMEA.

Build and maintain backend services and data pipelines for large-scale optimisation runs. Validate and integrate client trade data, develop APIs, improve performance and reliability, automate workflows, and generate reports delivered securely to clients.

As a Lead Python Engineer, you'll oversee technical direction, mentor a small team, and develop data pipelines and services while driving architectural discussions.

Design, implement and support front-office trading applications for the Derivatives Engine, focusing on hedging, risk management, pricing and ad-hoc trading tools. Work closely with traders, quants and technology to translate requirements into production-quality Python solutions, ensure testing and support plans, and help hand over tactical tools to central technology teams.

Design and develop software solutions, analyze complex problems, and provide support for trading activities in a dynamic environment.