Top Python Jobs in London

Design, build, and ship production-grade LLM-powered features (RAG, extraction, summarisation, agentic tooling). Build data and inference pipelines, integrate with back-ends and vector stores, ensure evaluation, monitoring, cost and performance optimisations, and champion testing and reliability alongside product and tech leads.

Short-term (3-6 month) R&D internship to develop and validate hyperspectral imaging algorithms and low-latency image processing tools, design and integrate intraoperative imaging systems, and execute validation tests using phantom models, ex-vivo and in-vivo experiments supporting a regulatory-cleared surgical imaging platform.

Design, develop, test, and maintain scalable R Shiny applications for clinical data review, visualization, QC, and reporting. Build reusable modules and utilities (R, SAS, Python), integrate cloud data sources, validate to regulatory/GxP standards, optimize performance, perform testing, and support study teams.

The Hydrologist role involves designing and maintaining quantitative forecasting tools for hydrological variables, integrating them into power generation forecasts. The role requires strong programming skills and familiarity with hydrological modeling.

Lead end-to-end pricing for complex, multi-class insurance portfolios. Partner with underwriters to design and optimise portfolio structures, assess pricing adequacy, monitor portfolio performance, develop benchmarks and pricing tools, derive expected loss ratios for planning, and mentor junior actuaries. Provide clear recommendations on deal structure, pricing, and capital efficiency while supporting governance and cross-functional collaboration.

Own and design the platform infrastructure for large-scale RL research: manage Kubernetes clusters, schedule GPUs at scale, improve resilience, run cloud infrastructure (GCP), implement observability, and build developer environments and tooling with Python and Rust.

Embed with enterprise clients to scope, architect, and deploy end-to-end AI workflows. Lead technical delivery, integrate data and models, prove ROI, and produce reusable patterns and documentation while collaborating with product, data science, and consulting partners.

Summer trading internship rotating across at least two commodity desks. Build and maintain analytics dashboards, back-test trading ideas, present results, and learn from senior and junior traders. Onsite in London with mentorship and potential full-time consideration.

Implement and customize an AI account-management platform for enterprise clients: deploy integrations, build data pipelines, modify AI models, troubleshoot, train customer teams, and feed product insights back to engineering and product teams.

Design, build, and ship full-stack features for a data-intensive orchestration platform. Implement APIs and integrations with CRMs and third-party systems, collaborate with data teams for signal processing, drive architecture and technical decisions, and help shape engineering practices as an early team member.

Lead client engagements to analyze large, complex datasets, build data pipelines and dashboards, automate marketing tracking, harmonize data, apply statistical methods (R/Python), and communicate insights to technical and non-technical stakeholders.

Six-month PhD internship on the Central Execution Desk performing execution research: analyzing market microstructure, building simulation and analytics tools, designing and evaluating models, and working with large noisy datasets to support trading and execution decisions.

Develop and deploy AI-powered applications, agents, and integrations for HFT. Evaluate models and tooling, build agentic workflows, integrate AI with internal systems, and collaborate with research and trading teams to deliver production-grade AI solutions and best practices.

Lead a team of five QA engineers and own quality strategy across multiple software product portfolios. Transform QA into an automation-first, AI-enabled function; establish standards, processes, and release quality gates; expand automated testing; and reduce manual regression. Collaborate with engineering and product leadership, develop team members, and contribute hands-on to test design, execution, and defect analysis for native desktop applications.

Design, deploy, monitor and support global Cisco-based voice and video collaboration infrastructure. Troubleshoot UC systems, integrate cloud conferencing, automate via APIs, manage upgrades, and liaise with IT teams to maintain system stability and documentation.

Lead QA test strategy and automation for long-range video surveillance cameras. Design and implement automated test pipelines, collaborate cross-functionally on requirements and schedules, analyze results, maintain metrics, conduct field testing, and improve CI/CD-driven firmware/software release quality.

Lead and deliver large-scale distributed infrastructure projects from ambiguous requirements to production. Make architecture decisions, ensure reliability, scalability, and security, collaborate with research/product teams, set technical standards, improve operational processes, and mentor engineers.

Support internal research and external drug discovery collaborations by applying computational chemistry, cheminformatics, and data analysis. Build tooling and datasets, evaluate molecular ML models, analyse large molecular and protein-ligand datasets, identify dataset leakage and failure modes, and turn analyses into actionable guidance for model development and drug discovery.

Work at the intersection of protein design and ML to support internal and client drug discovery programs. Apply and run protein design and modelling tools, analyse large sequence and structural datasets, build reproducible pipelines, select candidates for experiment, and connect experimental outcomes back to computational predictions to improve models and workflows.

Own and improve production infrastructure reliability, deployments, Infrastructure-as-Code, Kubernetes environments, automation, CI/CD, monitoring, alerting, and observability. Investigate incidents, optimize system performance, maintain documentation and runbooks, and support DNS, WAF, CDN, and caching infrastructure. The role requires strong Linux administration, Bash scripting, networking, Git, and containerization skills, with independent ownership and collaboration across development and operations teams.

Lead design and delivery of scalable analytics, BI, forecasting, and AI-driven solutions for RevOps Sales Intelligence. Partner cross-functionally to improve data quality, governance, automation, and self-service analytics using cloud data platforms and modern ML/Generative AI techniques. Manage roadmap, stakeholder communication, and operationalize AI/analytics at enterprise scale.

The Senior Machine Learning Engineer will design and implement ML pipelines, optimize LLM performance, and collaborate on AI infrastructure for cost-efficient solutions.

The DevOps Engineer designs and manages AWS cloud infrastructure, automates operations, develops CI/CD pipelines, and ensures robust monitoring and observability.

Provide expert, client-facing support for institutional clients using Valitana's Vantage platform. Analyze fixed income and structured finance metrics, assist with compliance and data uploads, build custom workflows and Excel solutions, and deliver white‑glove service to maximize platform value. Occasional travel (≈10%) for conferences and events.

The Anti-Bot Engineer will research and implement evasion techniques against sophisticated anti-bot systems, debug TLS fingerprinting, and analyze browser behavior to optimize scraping methods.