Top Python Jobs in London

Lead design, delivery and maintenance of on-prem and cloud market-data distribution systems (real-time, historical, AI/ML use cases). Collaborate with product/stakeholders, provide 3rd-level operational support, validate AI-assisted recommendations, ensure resiliency, security, auditability and strong process controls.

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.

Coordinate and manage delivery of technical security assessments (red/purple/penetration) across global teams. Maintain stakeholder communication, enforce SOPs, resolve SLA roadblocks, produce reports and management information, identify automation and process improvements, and build partnerships across US, UK and Singapore assessment teams.

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 development of low-latency equities algorithmic trading strategies and execution platform. Build simulation and automated testing, analyze and tune models/signals, support production incidents, and create scalable real-time processing solutions.

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 cloud-native Python applications for global commodities trading desks. Design, build, test, and operate resilient, secure production systems; drive automation and CI/CD; evaluate vendor solutions; and mentor engineering communities of practice.

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 applied AI/ML efforts to design, build, deploy, and maintain production ML services (NLP/CV focus). Collaborate with engineering, cloud, and SRE teams, research models, ensure MLOps best practices, and document processes for regulatory compliance.

Lead design and delivery of production-grade AI/ML and agentic solutions for the LLM Suite platform. Own architecture, implementation, and operational stability using public cloud services and agentic frameworks. Build GenAI services, integrate LLMs, ensure secure high-quality code, and improve platform scalability and reliability through collaboration and communities of practice.

Lead execution research and transaction cost analysis to inform algorithm roadmap. Build and govern TCA frameworks, run market-microstructure research and A/B tests, develop pre- and post-trade models, translate findings into production features, and partner with Product, Trading, and Technology while mentoring junior researchers.

Lead reliability, performance, and automation of analytics pipelines and reporting. Define SLIs/SLOs, monitor data quality/lineage, enable self‑service dashboards, enforce data risk/controls, drive FinOps/capacity insights, lead stakeholder reviews, and mentor junior engineers.

Lead metadata and information architecture by developing metamodels, controlled vocabularies, mappings, lineage, DSLs, and tooling. Prototype, validate, and integrate metadata into engineering workflows to enable AI, governance, traceability, and secure, reusable metadata-driven patterns. Influence standards and partner with stakeholders to ensure implementable models and controls.

Design, build, and operate cloud-native lakehouse and streaming data platforms and pipelines using Python, PySpark, dbt, Airflow, and orchestration/infrastructure tooling. Ensure performance, reliability, security, and observability; model and transform data for analytics; deploy with IaC, containers, and Kubernetes; mentor engineers and drive platform standards and best practices.

Build AI/ML-driven analytics and automation for CIB Treasury, Securities Services and Payments. Develop scalable data architecture, automate manual processes, create sequential decision-making tools to optimize net interest income, drive projects end-to-end from prototype to production, present commercial recommendations to stakeholders, and collaborate across Quant Research, Treasury, CIO, Technology and CDAO.

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 Markets Treasury optimization of liquidity, collateral, and capital across trading desks. Develop optimizers, analytics, and AI-enabled tools; review counterparty portfolios; influence methodology changes; partner with trading, technology, vendors, and clearing houses to maximize firm liquidity and capital efficiency while addressing regulatory constraints.

Design, develop and optimize a large-scale Monte Carlo engine for CVA/FVA calculations; implement numerical techniques for efficiency; build production-grade analytics; collaborate with traders, risk managers and tech teams; drive software engineering best practices.

Lead development and adoption of information architecture standards, controlled vocabularies, and governed data models. Collaborate with business and technical teams to evaluate technologies, provide technical guidance, influence product design, and standardize operational and analytical data management across the Corporate Investment Bank.

Lead design and deliver high-performance, low-latency trading services. Build secure, observable, resilient microservices, drive architecture, incident response, CI/CD, AI-assisted engineering adoption, and mentor engineers.

Lead design and delivery of secure, scalable cloud platform tooling and DevEx practices to support AI/ML and data platforms. Drive CI/CD, observability, cloud security, AI-assisted engineering adoption, paved paths, telemetry, and cross-team modernization while mentoring engineers and governing AI-assisted workflows.

Build and maintain a centralized risk platform that unifies data and decision-making across identity, fraud, and compliance. Develop foundational backend services, enable product teams to ship safely at scale, and collaborate with risk and product stakeholders to reduce fraud and improve trust.

Leads a 25+ person organization of data scientists, machine learning engineers, and analytics engineers across Identity and Financial Crime. Owns data quality, governance, lineage, observability, fraud and financial-crime models, production performance, monitoring, retraining, and domain data products. Partners with engineering, product, risk, compliance, legal, and central data teams; influences upstream teams, develops managers and senior ICs, handles incidents, and scales hiring and technical strategy.

Join Maven Securities as a graduate infrastructure engineer in London, specializing in Linux or networking. You will support trading-critical systems, tune operating systems and networks for low latency, automate infrastructure with Ansible, Python, and Bash, participate in incident response and on-call rotations, and build observability using Prometheus and Grafana. The program combines structured training with hands-on work in a fast-paced proprietary trading environment.