Top Python Jobs in London

Build and maintain Python services that automate finance and operations workflows. Deliver features end-to-end, write tests, participate in code review, support production systems, collaborate with Finance/Operations stakeholders, and grow ownership of components toward senior responsibility.

Design and maintain behavioural event models and Databricks pipelines to ensure high-quality, observable customer data. Own event schemas, data quality checks, semantic metrics, and instrumentation alignment to support analytics, experimentation, and self-serve reporting.

Own a from-scratch rebuild of Nory's mobile app using React Native and TypeScript, ensuring reliability on low-spec devices and poor connectivity. Set architecture, design system, and migration plan, ship an MVP, integrate AI agents to speed development, and grow the codebase into a platform for cross-functional teams while maintaining high UX and reliability standards.

Accountable risk leader for the UK entity, overseeing enterprise risk framework, regulatory and prudential requirements, outsourced Group risk service delivery, and risk MI. Provide 2nd-line oversight across financial, operational, conduct, and third‑party risks, engage with the FCA, and build local risk capability while managing intra‑group service quality and regulatory compliance.

Design and backtest ESG, climate, and sustainability-focused index methodologies. Conduct quantitative research, manipulate large datasets, and support index construction using Python and SQL. Collaborate with data, product, and sales teams, participate in client projects to refine methodology, and contribute to new product development and sustainability research.

Design and build automation-first test strategies and frameworks for cloud-native, containerised, and air-gapped Google Distributed Cloud environments. Implement automated tests across unit, integration, system and infrastructure levels, integrate testing into CI/CD pipelines, validate IaC and policy-as-code, embed security and compliance checks, produce test reports and dashboards, participate in architecture discussions, and support defect triage and continuous improvement.

Work across the full software lifecycle to build robust margin and collateral solutions. Collaborate with product teams and users, design and implement RESTful APIs, apply TDD and clean code, and handle data structures, algorithms and SQL. Adapt quickly in a fast-paced, changing environment and contribute to a strong global engineering culture.

Lead portfolio management for a growing unsecured lending product (Flex), building forecasting and monitoring frameworks, risk-based pricing, lifecycle engagement and loss-mitigation strategies. Manage and grow a team of credit analysts, collaborate across product squads, and partner with Legal, Compliance and 2nd Line Risk to scale the portfolio safely while driving commercial outcomes.

Lead product analytics to drive growth: own KPIs, design and analyze A/B tests, identify funnel improvements, build dashboards and self-serve analytics, influence product strategy, and mentor junior analysts to embed experimentation and data-driven decision making.

Lead credit risk strategy for Flex originations: set distribution and pricing frameworks, design real-time underwriting, oversee economic modelling, hire and grow an analytics team, and report performance to senior leadership.

Lead enterprise Java full-stack strategy and modernization (cloud, microservices, event-driven, API platforms). Champion AI-driven development and LLM/GenAI tooling. Oversee backend (Java) and frontend (React/Angular) delivery, ensure secure scalable design, CI/CD and DevSecOps practices, reduce technical debt and vulnerabilities, and lead large-scale programs for velocity, reliability, and cost efficiency.

Design and implement scalable IAM architecture and tooling for AuraDB. Integrate enterprise SSO providers, implement auth protocols (OAuth2, OIDC, SAML, LDAP, MFA), manage lifecycle (SCIM), develop access control models (RBAC/ABAC/PBAC/ReBAC/FGA), enforce least-privilege, apply zero-trust patterns, ensure SOC 2 compliance, mentor engineers, and collaborate across product, security, and infrastructure teams.

Provide first-line supervision of trading across Ring and LMEselect, determine reference, closing and auction prices, enforce dealing regulations, manage error trade and dynamic price band procedures, perform quantitative analysis and automation to support pricing, benchmark administration, market surveillance, and stakeholder support.

Own and design Bazel rules and extensions to scale a multi-language monorepo (Python, C++, Rust, SystemVerilog) for ML, kernel, and simulation workloads. Build reproducible multi-language build pipelines, optimise CI across large compute clusters, define developer build experience, and contribute upstream to Bazel rules.

As a Quantitative Research Associate, you'll support portfolio managers by developing quantitative tools, performing data analysis, and assisting with portfolio analytics. The role involves maintaining databases, ensuring data quality, and collaborating with technology teams to enhance data infrastructure.

Design, build, and maintain services and infrastructure for AI-powered agents. Develop Python backend services and tested APIs, support React front-end integrations, work with MySQL, implement RAG and vector DBs, apply prompt engineering, optimise LLM interactions, and monitor production reliability and performance.

The role involves managing CI/CD pipelines, collaborating with teams, ensuring software delivery through automation, and mentoring engineers. Significant experience in Azure DevOps, Salesforce, and scripting is essential.

Design, build, and deploy production-grade generative AI solutions on Azure/Databricks. Implement agentic workflows, RAG pipelines, and LLM-backed APIs using LangGraph, FastAPI, Docker, Kubernetes, and search/retrieval systems (Azure AI Search, MongoDB). Collaborate across product, data science, and engineering, owning design, testing, deployment, and monitoring with a strong focus on reliability and maintainability.

Build and scale MLOps infrastructure and CI/CD for ML models, support data pipelines, model training and serving on cloud, implement monitoring and automated retraining, optimise workflows for performance and scalability, and collaborate with data scientists and engineers to productionise ML solutions.

Manage the lifecycle of sustainable finance products: lead cross-functional product development, monitor performance and sustainability metrics, engage institutional clients and stakeholders, support sales/marketing, drive market research and thought leadership, and guide product specialists to deliver investment solutions aligned with regulatory and market trends.

Perform quantitative analysis using BMLL's product suite and large market-data lakes to support sales/marketing, prototype technical solutions, gather client feedback, and contribute to the product roadmap. Communicate results to technical and non-technical stakeholders, maintain reproducible documentation, and manage complex projects under tight deadlines.

The Senior Engineer will design, develop, and deploy software solutions for a global financial platform, collaborating with cross-functional teams and maintaining high-quality standards.

Lead design, build, and scale centralized SRE platforms and internal tools to improve operational efficiency, resiliency, monitoring, automation, and runbooked support for production services. Hands-on full‑stack development, infrastructure automation, observability, SLIs/SLOs, AIOps, and cross-team partnership to reduce toil and improve reliability.

Lead a team of engineers to build and maintain the MLOps platform and production pipelines for credit risk modelling. Provide technical leadership, hands-on engineering, and cross-disciplinary collaboration to improve reliability, observability, reproducibility, and speed of experimentation.

Lead a small engineering team building the platform, pipelines, and production systems for credit risk models. Provide hands-on technical leadership, raise engineering standards (reliability, observability, reproducibility), mentor engineers, and collaborate with data scientists and analysts to improve experimentation speed and model-driven lending decisions.