Top Python Jobs in London

Lead and scale a Forward Deployed Engineering team embedded with strategic enterprise customers. Own technical discovery-to-close, ensure delivery quality, coach FDEs, manage stakeholder relationships up to C-suite, build repeatable playbooks, and train partner GSIs to extend the motion at scale.

Embed with customer engineering teams to discover bottlenecks, design and ship production-grade AI-native Cursor workflows end-to-end, ensure production reliability, monitor and debug model behavior, and generalize solutions back into the core product.

Lead development of advanced analytics and machine learning models, create algorithms from scratch, analyze large datasets and relational databases, interpret results and recommend solutions, act as domain expert, perform manual claim selection and audit review, and generate new selection concepts/queries. May lead and manage a team.

Design, develop, and support enterprise-grade applications using .NET/.NET Core and Python; define application and data architectures (relational, NoSQL, data lakes, streaming); advise on on‑prem and cloud infrastructure (VMs, containers, Kubernetes/AKS, Azure); drive SDLC/DevOps practices and CI/CD; troubleshoot cross-layer issues; collaborate with stakeholders to deliver resilient, secure, and scalable insurance-focused solutions.

Design, develop, configure and maintain Earnix-based pricing and decisioning solutions for P&C insurance. Integrate Earnix with enterprise systems, build data ingestion and model deployment pipelines, support testing, production reliability, troubleshooting, and documentation while collaborating with actuarial, data, and IT teams.

Lead design, implementation and optimisation of Earnix pricing solutions for P&C insurance. Provide technical leadership on architecture, model deployment, integrations, data ingestion, monitoring, testing, and production support. Collaborate with actuarial, underwriting, data, and IT teams to ensure scalable, reliable pricing and decisioning systems and contribute to governance and documentation.

Design, build, and operate multi-region AWS platforms and cloud networking (VPCs, Transit Gateway, PrivateLink). Automate infrastructure with IaC, implement security controls, enable cross-region routing, ensure HA/DR, monitor with CloudWatch/X‑Ray, troubleshoot networking/latency, and collaborate on CI/CD and platform self-service.

Senior production-facing engineer responsible for implementing and operating scalable, resilient software systems. Lead adoption of engineering best practices, CI/CD, GitOps, IaC, containerisation, observability, incident and problem management, and Agile delivery across global teams while coaching peers and managing stakeholders.

Design and execute manual and automated tests for Google Cloud Identity. Perform API and security testing, identify and report bugs and vulnerabilities, validate fixes through regression testing, participate in code reviews, automate test processes, and document test cases and findings.

Design and implement automated tests and test strategies for GCP-integrated applications. Create test data/environments, run integration and performance testing, troubleshoot defects with developers, improve CI/CD pipelines, and document test plans and results.

Lead development of a generative AI proof-of-concept and integrate AI solutions into software workflows. Mentor data engineers, design and implement Python-based generative AI applications, build scalable data pipelines, deploy models/APIs, and translate stakeholder requirements into technical solutions while staying current on AI and data engineering best practices.

This role involves end-to-end ownership of product delivery, focusing on backend systems, LLM integration, and cross-functional collaboration in a full-stack capacity.

Design, develop, and deploy scalable SaaS applications while implementing DevOps practices, ensuring high performance, and collaborating with the product team to enhance user experience.

The Senior Software Engineer will architect and build backend systems, own clinical system integrations, improve performance, and shape engineering culture.

The Platform Engineer will build and scale the infrastructure for AI-driven drug discovery, focusing on Kubernetes management, cloud infrastructure, and automation using Python and Bash.

Develop ML systems for autonomous driving, lead projects using RL and deep learning, and collaborate with cross-functional teams to improve Waymo's technology.

Work as a Full Stack Developer building web and mobile applications for clients. Collaborate with small agile teams, engage directly with stakeholders, deliver bespoke solutions, and grow into mentoring and technical advisor responsibilities while expanding technical skills.

Design and build a greenfield marketing data foundation: tracking/tagging, identity strategy, data pipelines, integrations (GA4, Salesforce, Databricks, COGNOS), data modeling and QA, dashboards, attribution/MMM, governance and compliance. Serve as primary contact for marketing data issues, enforce standards, prioritize analytics backlog, and enable stakeholders.

Develop and integrate pricing models within a resilient, real-time event-driven framework. Contribute to quantitative libraries, implement data-driven development (unit tests, mocking, back-testing), collaborate with quants, data scientists, and engineers, research innovative solutions, and ensure conduct risk awareness while working with global teams.

Hands-on role building cloud-first and ML infrastructure: develop high-availability automation workflows, resilient big-data pipelines, support developer systems for agentic applications, drive DevSecOps, architect security and identity frameworks, and optimise cloud cost and performance.

Analyze and map current workflows to identify automation opportunities; design, prototype and deploy AI-driven solutions and Copilot agents using low-code tools; collaborate with IT, Data and Operations; measure efficiency gains and support change management, training and adoption.

Join the Integration team to collect, validate, map and load high-volume financial data from clients into SteelEye's analytics platform. Engage stakeholders, resolve data queries, refine operational processes, and perform industry research to meet data quality metrics.

Serve as the technical lead in pre-sales for DACH customers: assess technical fit, support trials, design solutions and integrations, create product examples/documentation, provide product and market feedback, and improve pre- to post-sales transitions.

Lead Data Analyst responsible for advanced analytics, quantitative analysis, experimentation, and commercial modeling. Partner with Data Engineering to implement scalable pipelines, drive insights into decision-making, and embed customer research into operations. Mentor analysts, build data literacy, and influence cross-functional strategic and tactical decisions.

Design and scale end-to-end AI/ML solution patterns, data pipelines, and model architectures; lead pilot MVPs/PoCs into production; define AI governance and best practices; collaborate with architects, engineering, and sales to drive services growth.