Top Python Developer Jobs in London

Leads software engineering for a secure, scalable cloud data platform supporting credit risk technology. Responsibilities include designing and delivering Python solutions, reviewing production code, building CI/CD pipelines, improving application resiliency, standardizing SDLC tooling, automating remediation, and guiding responsible AI-assisted engineering practices. The role also evaluates vendors and leads cross-functional delivery initiatives involving AWS, Databricks, Spark, Terraform, lakehouse architecture, observability, security, and governance.

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

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.

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, 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.

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 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.

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.

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.

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 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.

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.

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 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.

Join Markets Technology Trading as a Software Engineer II to design, develop, and troubleshoot secure, stable, high-quality Python code. Leverage enterprise AI-assisted development tools, collaborate in an Agile team, handle data processing and production engineering tasks, and apply SDLC practices to build performant, resilient applications while learning domain systems and secure coding standards.

Design, develop, and maintain secure, scalable software solutions as a seasoned agile team member. Produce high-quality production code, architecture/design artifacts, data visualizations, and troubleshooting. Use CI/CD, resilient application patterns, enterprise AI-assisted development tools, and validate AI outputs. Contribute to engineering communities and improve code hygiene and system architecture.

Build and maintain secure, scalable Flow Registry features for GitLab’s agentic platform using Python, LangChain, and LangGraph. Design GraphQL and REST APIs, improve automated testing, optimize PostgreSQL-backed applications, and collaborate across backend, frontend, product, UX, and AI teams. Participate in Tier 2 on-call support, troubleshoot production issues, improve observability, and contribute to responsible AI integration in GitLab’s remote, asynchronous environment.

Seeking a Senior Software Engineer for the Beacon Core Engineering team to design, build, deploy, and maintain software tools and assist clients using AI agents, focusing on quality code and collaboration.

Lead design and implement IAM-focused public cloud platform services. Develop and maintain production code, drive architecture and reliability, automate remediation, lead reviews, mentor engineers, and collaborate across platform and trust teams to deliver secure, scalable cloud services.

Build and maintain secure, production-ready ML solutions and cloud frameworks using Python and AWS. Collaborate with data scientists, platform teams, SRE, and engineering communities to integrate, deploy, monitor, and scale AIML systems following SDLC and MLOps practices.