Top Python Jobs in London

Develop and maintain quantitative trading strategies, pricing models, backtesting frameworks, and market simulations for cryptocurrency markets. Collaborate with traders and quantitative researchers to convert ideas into production software, manage strategy deployment and real-time tuning, and analyze performance data to improve returns and generate alpha. The role requires strong software engineering, quantitative finance, market microstructure, and algorithmic trading expertise.

Design and scale AI-driven solutions for fashion imagery and video, using computer vision, deep learning, generative AI, LLMs, and multimodal processing. Build reliable data pipelines, fine-tune and deploy models, standardize AI workflows, implement MLOps practices, develop evaluation frameworks, and partner with cloud infrastructure and product teams. Promote responsible AI by identifying and mitigating bias and risk while delivering production-ready, personalized shopping experiences.

Supports quantitative trading analysis by analyzing proprietary market data, developing execution signals and impact models, creating visualizations, backtesting trading features, and evaluating algorithm performance. The intern will work with quantitative analysts on execution strategies, market microstructure, statistical analysis, and client-facing research materials. This is a 12-week, New York-based internship requiring advanced Python, pandas, SQL, computational finance, statistics, mathematics, and exposure to machine learning.

Lead the AI function, define roadmap, and build production generative AI systems (voice, chat, document). Split time between hands-on engineering (~40%) and leading the AI team, establishing RAG/agentic workflows, evaluation, observability, and best practices. Collaborate with product and engineering to deliver reliable, user-focused AI features.

Lead technical implementations and onboarding for a SaaS finance platform, including configuration, testing, UAT, cutover, hypercare, customer support, troubleshooting, documentation, automation, and product feedback. Translate accounting challenges into platform solutions while collaborating with Customer Success, Product, and Engineering. The role requires software implementation project management, accounting ERP expertise, technical problem-solving, customer-facing communication, and basic scripting knowledge.

Designs, implements, and optimizes Microsoft Purview data governance, compliance, and risk solutions across Microsoft 365 and Azure. Responsibilities include DLP, information protection, insider risk management, eDiscovery, communication compliance, data classification, lifecycle management, and information barriers. The consultant assesses environments, creates technical documentation, advises stakeholders, presents recommendations, integrates Microsoft services, and provides ongoing customer support and advisory services.

Build and operate ML platform capabilities that move models from experimentation into reliable production. Responsibilities include automating training, validation, deployment, retraining and rollback; operating batch and online inference services; implementing CI/CD, observability, monitoring, alerting and incident response; improving scalability, security and cost efficiency; and developing production-grade Python tooling. The role collaborates with research, software, platform, security, data and product teams.

Design, build, and operate reliable, scalable AI/ML platform infrastructure. Develop observability, resilience, security, and automation tooling, own NFRs, participate in on-call rotations, troubleshoot production issues, and collaborate cross-functionally to support large-scale AI deployments.

Leads market intelligence, competitive analysis, portfolio analytics, and pricing research for Wiley’s Clinical Outcomes Assessment business. Builds datasets, dashboards, and AI-assisted analytical workflows to identify portfolio opportunities and inform product roadmaps, partnerships, and commercial positioning. Collaborates with product, commercial, scientific, licensing, and delivery teams to translate complex clinical, regulatory, and market data into defensible recommendations for senior stakeholders.

Design, build, and deploy Generative AI/LLM applications (chatbots, RAG, document intelligence) on GCP using Vertex AI, BigQuery, and containerised services. Work with data teams, implement CI/CD, monitoring, vector databases, and ensure Responsible AI, GDPR, and regulatory compliance while engaging stakeholders.

Build and deliver full-stack applications with generative AI and agentic capabilities. Collaborate with engineers, business professionals, clients, and end users to identify valuable AI use cases, design solutions, develop and test software, deploy applications, and iterate based on feedback. Write maintainable code, apply sound engineering judgment, evaluate AI-assisted development tools, and contribute to reliable enterprise workflows using APIs, retrieval, databases, and AI integrations.

Build, validate, and enhance credit risk, statistical, and machine learning models for credit decisioning, portfolio monitoring, capital computation, and impairment forecasting. The role partners with Risk, Product, and Data teams to productionize models, improve IRB and IFRS 9 outputs, streamline analytics pipelines, establish monitoring indicators, and produce audit-ready documentation.

Build and deploy production-grade AI agents, LLM applications, automated workflows, Python services, integrations, and scalable enterprise systems for internal tax teams. Responsibilities include API and event-driven integrations, workflow orchestration across Azure platforms and Copilot Studio, reusable connectors, CI/CD, monitoring, technical design, code reviews, and mentoring early-career engineers.

The Financial Risk Manager will support the Risk team with ETD project reporting, risk monitoring, and ensuring awareness of financial risk responsibilities.

Designs, develops, tests, deploys, and supports front-office applications and databases. Provides expert system analysis, architecture, application performance optimization, security compliance, and production support. Collaborates with portfolio managers, product owners, and cross-functional teams; evaluates build-versus-buy options; mentors engineers; resolves escalated issues; and recommends enterprise-wide improvements. The role requires full-stack expertise across frontend frameworks, Java-based backend services, cloud platforms, databases, CI/CD, and automated testing, plus an understanding of investment products and trading workflows.

Leads complex cyber investigations, incident response, threat hunting, forensic analysis, and detection engineering across endpoint, network, cloud, and identity environments. Owns optimisation of Microsoft Sentinel, Defender, SIEM, SOAR, EDR, and XDR capabilities, including detection rules and response automation. Provides technical leadership, mentoring, governance support, threat intelligence, reporting, and stakeholder communication while improving customer security posture and operational resilience.

Lead Field’s quantitative analysis function for battery storage and renewable energy assets across European power markets. Own pricing, valuation, risk, systematic trading, and battery spread-option models; oversee volatility, hedging, back-testing, scenario analysis, VaR, and CFaR reporting. Support structured product origination, market entry, and commercial pricing while managing market data infrastructure, ETRM implementation, model governance, and a junior quantitative professional.

Creates photorealistic face-swap imagery using deep-learning technologies and proprietary visual effects tools. Responsibilities include preparing and curating image datasets, creating model evaluation templates, monitoring and configuring training, assessing quality, and producing face-swap slap composites for review. The role also involves learning new tools, experimenting with workflows, and potentially automating repetitive tasks using Python.

Leads complex enterprise customer engagements as a senior technical authority for data and AI architecture. Drives Databricks adoption, consumption strategy, executive relationships, competitive positioning, and large-scale architecture initiatives including migrations, real-time systems, lakehouse platforms, and AI/ML. Orchestrates cross-functional teams, mentors solutions architects, influences senior stakeholders, and contributes to technical thought leadership.

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.

Build and maintain infrastructure, tools and services to support AI research and engineering. Deploy and operate services with Kubernetes and Docker, manage cloud infrastructure (Terraform/AWS), support CI/CD, and enable ML application deployment, orchestration, and accelerator hardware management.

Lead technical pre-sales for Datadog in the public sector: deliver demos and POVs, own trial engagements, qualify and technically close complex opportunities, present product briefings, record CRM feedback, and collaborate with sales and product teams while traveling up to 45%.

As a Quantitative Research Analyst, you will develop and test automated trading strategies using statistical techniques and algorithms, conduct research, and analyze data.

Conduct quantitative research and statistical analysis on crypto markets to develop, backtest, and implement automated trading strategies. Work with large and unconventional data sets to build valuation models, refine algorithms, and deploy signals in live trading to drive P&L growth.

Conduct research, statistical analyses, and develop automated trading strategies using large data sets and sophisticated statistical techniques.