Top Python Jobs in London

Turn data into product and business decisions through metric definition, experimentation, causal analysis, deep-dive research, and clear recommendations. Model reliable analytical data in dbt, build scalable dashboards and reporting, partner with data engineering, and influence leaders across Product, Finance, Growth, GTM, and Customer Success. The role also helps establish data-team standards, tooling, and operating practices in an AI-first environment.

Own customer implementations from solution design through deployment and adoption. Build proofs of concept, integrations, custom workflows, data pipelines, and production-quality code using APIs and webhooks. Lead technical workshops, document solutions, troubleshoot live customer systems, provide on-call support, and convert recurring customer needs into reusable product improvements. The role is embedded with major internet platforms and requires strong engineering skills, customer ownership, initiative, and comfort with ambiguity.

Own database performance, scalability, availability, disaster recovery, and cost across a production SQL Server estate on Azure. Optimize queries, indexes, execution plans, and latency-critical card and ledger workloads; design horizontal scaling and HA/DR strategies; build diagnostic and automation tooling; lead incident response; and guide engineers on data access, ORM performance, migrations, and database architecture.

Design, build and maintain Microsoft Cloud applications using Power Apps/Pages and Azure services. Implement Entra ID authentication, Azure Functions/App Services, Logic Apps and Blob storage. Create Azure Data Factory pipelines and APIs (REST/GraphQL), integrate external services and LLM data pipelines, and ensure performance, security, and scalability while collaborating with customers to turn requirements into streamlined apps.

A 12-week trading internship working alongside discretionary and quantitative traders across equities, credit, futures, and macro instruments. The intern will analyze market and proprietary trading data, study market microstructure, improve AI and quantitative trading tools, and identify execution enhancements. Responsibilities include collaborating with systematic and discretionary investment teams, developing AI-driven workflows, and clearly communicating research findings. Candidates should have strong mathematics, statistics, machine learning, Python, interpersonal, and financial-market skills.

Leads hands-on software engineering across data technology platforms, guiding architecture, development, testing, releases, infrastructure as code, and operational support. Partners with stakeholders to translate business needs into scalable, secure applications and data pipelines. Establishes engineering standards, manages technical debt and application lifecycles, resolves cross-team dependencies, and mentors engineers and third-party partners. Requires strong expertise in C#/.NET, Python, Node.js, or React; Azure cloud-native development; Snowflake; integration services; automated testing; and agile delivery.

Provide advisory and quantitative modelling for GB power markets, with a focus on battery storage and other flexible power assets. Assess revenues, optimisation strategies, investment cases, valuations, and commercial structures using wholesale, balancing, reserve, response, and capacity market knowledge. Translate regulatory and market developments into client recommendations, contribute to due diligence, and present technical findings clearly to senior stakeholders.

Manage liquidity forecasting, stress testing, treasury risk models, funding, cash positioning, and FX dealing. Own treasury risk controls, remediation, policies, and procedures while contributing to ICARA, wind-down planning, and risk assessments. Analyze new product liquidity risks, improve dashboards and reporting with Excel, SQL, Power BI, Python, and AI tools, and present recommendations to senior stakeholders.

Lead the design, deployment, and scaling of machine learning and generative AI systems. Guide end-to-end model development, architecture, production reliability, MLOps, and engineering excellence. Mentor engineers, shape technical roadmaps, collaborate cross-functionally, and drive organization-wide AI adoption. The role combines hands-on technical leadership with people management in a fast-paced startup environment.

Develop backend services and APIs powering AI and generative AI tools, while improving search performance, scalability, and reliability. Design data flows, integrate ML services, work with Elasticsearch and PostgreSQL, and contribute to architecture, access control, and visualization infrastructure. Lead code reviews, mentor engineers, refactor systems, document solutions, and own projects through delivery while collaborating with product, design, data science, and business teams.

Build and maintain CI/CD pipelines, cloud and on-premise infrastructure, infrastructure-as-code, monitoring, alerting, and logging for trading and business systems. Support developers with build, deployment, and environment issues; automate repetitive tasks through scripting; improve software delivery practices; investigate incidents; and participate in an on-call rotation. The role requires experience with GitHub Actions, containers, Terraform, cloud platforms, networking, and Python, PowerShell, or Bash.

Design and deliver data architectures, cloud platforms, pipelines, integration frameworks, and governance practices for regulated-industry clients. Advise on data strategies, operating models, lakehouses, warehouses, data mesh, and AI-ready ecosystems. Lead transformation engagements, support pre-sales and thought leadership, mentor consultants and engineers, and maintain expertise in emerging data, cloud, analytics, and AI technologies.

Lead the design, implementation, and operation of Quantum’s Google Cloud platform. Responsibilities include GCP infrastructure and networking, Terraform-based infrastructure as code, CI/CD, Kubernetes, observability, security, incident response, developer self-service tooling, and platform migrations. The role also manages an infrastructure engineer, establishes platform standards, shapes the roadmap, supports regulatory compliance, and helps grow the platform team.

Leads a global production services and application support organization for mission-critical FX execution platforms. Responsibilities include improving incident, problem, change, and escalation management; driving automation, AI, observability, resiliency, and platform stability; partnering with engineering and business stakeholders; and overseeing service availability, SLA performance, risk controls, and talent development.

Work with ML and AI engineers to design and run experiments, evaluate and refine LLM-powered agent workflows, perform feature engineering and statistical analysis, prototype ML pipeline features, investigate production failure modes, and document reproducible findings that drive improvements in underwriting systems.

Leads enterprise data engineering strategy and global teams responsible for real-world data movement, lakehouse architecture, ingestion, transformation, storage, optimization, governance, security, and standardized data products. Partners with technology, analytics, and business stakeholders across clinical, safety, financial, and corporate domains. Oversees delivery prioritization, roadmaps, quality, reliability, team development, and scalable cloud data solutions using Azure Databricks, SQL, Python, and CI/CD.

Provides real-time first-level support for trading, market data, and technology issues during Cboe’s US market coverage session in London. Responsibilities include monitoring production systems, onboarding clients, provisioning services, conducting certification and deployment testing, coordinating incident resolution, documenting issues, and supporting trade operations. The role also involves weekend testing, vendor and participant communication, and oversight of low-latency trading systems.

Build and deploy production-grade machine learning software, tools, infrastructure, and scalable reusable solutions. Collaborate with engineers, data scientists, commercial leads, clients, and partners to solve complex challenges. Lead technical scoping, architecture, ML deployment standards, and customer advisory activities while translating technical concepts for non-technical stakeholders.

Build and deploy production-grade machine learning software, tools, infrastructure, and scalable solutions. Collaborate with engineers, data scientists, commercial leads, clients, and partners to solve complex challenges. Lead technical scoping, architecture decisions, and ML deployment standards while advising technical and non-technical stakeholders on production AI systems.

Build and scale AI-powered systems in cross-functional teams. Responsibilities include developing scalable frontend and backend architectures, deploying machine learning outputs into production, building data pipelines and cloud infrastructure, and advising customers on complex technical requirements. The role requires ownership across the application lifecycle, balancing rapid prototyping with maintainability, and collaborating with data scientists and product managers.

Lead the creation and growth of Genomics England’s SRE capability. Build and manage an initial team, identify high-value reliability improvements, establish engineering standards and tooling, reduce operational toil, and embed SRE practices across product squads. The role provides hands-on technical leadership across cloud and on-premise platforms, CI/CD, infrastructure as code, monitoring, resilience, and operational strategy while coaching teams and influencing engineering direction.

Leads the strategy and architecture for scalable search and AI retrieval systems. Responsibilities include selecting search technologies, designing keyword, vector, hybrid, and graph retrieval, developing knowledge graphs, building LLM-powered enrichment and recommendation pipelines, and architecting GCP data infrastructure. The role requires strong Python skills, production search experience, cloud-native expertise, and technical leadership.

Build, deploy, operate, and maintain scalable Palantir infrastructure and services for UK government customers. Responsibilities include monitoring, configuration management, product deployments, migrations, troubleshooting production issues, participating in on-call support, improving reliability, and automating manual workflows. The role also develops infrastructure solutions within Foundry and Apollo while collaborating with forward-deployed and product teams.

Analyze Sportsbook product performance through testing, reporting, ad hoc queries, and data-driven insights. Partner with product stakeholders, Analysts, and Data Scientists to optimize user experiences, track metrics, solve business problems, and support product development. Present findings to senior stakeholders and serve as a trusted source of analytical intelligence.

Supports test planning and execution for audio hardware and software products, including functional, performance, and regression testing. The role identifies defects, documents test results, communicates findings with engineering and product teams, and contributes to quality improvements in an audio manufacturing environment.