Top Python Jobs in London

Lead delivery of real-time flood forecasting projects: assess existing models and data, build/calibrate forecasting and hydrological models, produce technical reports, mentor staff, support bids, and maintain client relationships while ensuring compliance with Environment Agency requirements.

Lead and deliver real-time flood forecasting projects: develop, calibrate and review hydrological and hydraulic models, analyse hydrometric data, produce technical reports, mentor staff, support tenders, and maintain client relationships.

Lead delivery of real-time flood forecasting projects: assess existing models/data, develop and calibrate forecasting and hydrological models, produce technical reports, mentor staff, support bids, and maintain client relationships.

Senior Flood Modeller to develop and check hydraulic and water quality models (fluvial, coastal, surface water, drainage), lead hydrological assessments, scope modelling approaches, contribute to tenders, mentor junior staff, manage client relationships, and deliver projects on programme and budget using industry-standard software and project management systems.

Front-office quantitative role supporting mortgage and ABS trading across European markets. Build and implement mortgage pricing, loan-level cashflow and recovery models, automate pricing/surveillance frameworks, and work closely with portfolio managers on complex non-agency and illiquid securitized assets.

Lead technical onboarding of enterprise clients across Europe: map client data and business rules, design ingestion and integration layers, handle localisation and regulatory complexity, document reusable patterns, feed product feedback, and use LLM tooling to accelerate integration.

Lead development and global rollout of core C++ trading platform components. Implement exchange connectivity (FIX, WebSocket, HTTP), optimize for ultra-low latency (kernel bypass, TLS), build trading algorithms, improve reliability and observability, and collaborate closely with quants and traders to deploy production systems.

As a Data & AI/ML Architect, you will conduct workshops, oversee project delivery, design architectures, and mentor juniors while engaging with clients.

Architect, design and deliver production-grade AI solutions using ML, generative and agentic AI. Lead technical delivery, implement RAG/prompt engineering, ensure responsible AI, drive cloud-native, scalable architectures, mentor and manage junior engineers, and collaborate with clients to translate business needs.

Develop and benchmark 3D computer vision and localisation systems (SLAM, visual odometry, scene matching, geo-registration, 3D reconstruction). Extend geometric deep learning and feature-matching methods, design experiments and benchmarks, adapt models to hardware constraints, and collaborate with research, engineering, and product teams to deploy robust AI solutions.

Lead end-to-end implementation of AI tools for Macro & Fixed Income PMs: build prompts, datasets, vector stores, and pipelines; deliver training and playbooks; drive adoption and measure ROI; liaise with central AI/Tech and Compliance to ensure governance and production-grade deployments.

As an Engineering Manager, you will lead high-performing teams, mentor engineers, set engineering best practices, and ensure rapid project delivery while collaborating cross-functionally.

Manage and scale systematic trading strategies for global agricultural commodities by combining fundamental research with quantitative modeling, alternative datasets (weather, satellite, logistics), rigorous backtesting, portfolio construction, risk management, and productionalizing research into live trading systems.

Owns global intelligent PDU infrastructure, including monitoring, alarming, firmware and security updates, lifecycle planning, inventory strategy, technology standards, and vendor support. Develops capacity plans, forecasts infrastructure demand, manages replacements and RMAs, and supports incident response. Partners with engineering, procurement, logistics, business units, and operations to maintain reliable, scalable, and supportable data center power infrastructure.

Provides principal-level technical leadership for enterprise machine learning systems supporting a matching platform. Responsibilities include ML architecture, feature pipelines, model development, AWS SageMaker MLOps, low-latency inference, explainability, governance, security, monitoring, drift detection, testing, and deployment automation. The role mentors engineers, establishes engineering standards, and drives scalable, reliable, compliant ML platform capabilities across environments.

Build and deploy commercial data science models for churn prediction, cross-sell and upsell, revenue attribution, and next-best-action recommendations. Design experiments and causal analyses, productionize models on AWS, manage feature engineering and monitoring, and communicate insights to Sales, Marketing, Customer Success, Finance, and executive stakeholders. The role requires strong Python, SQL, statistics, cloud data warehouse, and production machine learning experience.

Build and maintain market, economic, research, news, and internal data pipelines for AI and quantitative models. Design scalable data architectures, implement data quality controls, manage metadata and lineage, create APIs and model-ready datasets, and optimize pipeline performance. Collaborate with data scientists and analysts while ensuring data security, privacy, permissions, and reliable integration across tools and models.

Design, build, evaluate, and deploy AI systems for enterprise customers. Responsibilities include creating prototypes and production integrations, diagnosing implementation issues, making decisions about reliability, security, governance, latency, cost, and safety, and helping customers scale adoption. The role also develops reusable architectures, tooling, playbooks, and technical guidance while partnering with customer and internal engineering, product, research, security, and go-to-market teams.

Lead security engineering efforts across the product lifecycle by conducting security reviews, threat modeling, vulnerability research, code analysis, fuzzing, secure design, and cloud infrastructure hardening. The role guides engineers and staff on secure practices, develops security tests and protocols, integrates security capabilities into CI/CD pipelines, and improves vulnerability detection using manual, automated, and AI-assisted techniques.

Conduct outbound prospecting to global enterprise businesses, qualify and progress early-stage opportunities, and support event and inbound follow-up. The role focuses on testing personas, refining messaging, designing outreach sequences, automating research and enrichment, improving GTM efficiency, maintaining HubSpot data quality, and helping define new markets and ideal customer profiles. It offers significant exposure to account executives and senior management in a fast-growing startup environment.

Lead and grow a team of Data Scientists, Data Engineers, and ML Ops Engineers developing large-scale autonomy data pipelines and machine learning solutions for perception and planning. Define technical direction, data strategy, evaluation frameworks, and production processes. Guide data collection, curation, labeling, sampling, model deployment, and optimization while ensuring scalability, cost efficiency, safety, and quality. Collaborate globally across infrastructure, simulation, tooling, and autonomy teams.

Leads and develops an Analytics Engineering team, owning prioritization, technical roadmaps, data product quality, modeling, testing, observability, documentation, and CI/CD practices. Partners with Product, Engineering, Analytics, Data Engineering, and Data Science to deliver scalable analytical data products and business value. Drives continuous improvement, knowledge sharing, standards, and adoption of emerging technologies including AI-assisted development.

Design, build, and operate Anthropic’s MCP proxy, OAuth, and token-management infrastructure. Own reliability through SLOs, on-call support, incident response, and postmortems. Develop enterprise administration controls and permission models, contribute to the MCP standard and Python and TypeScript SDKs, scope complex multi-month initiatives, drive cross-organizational alignment, and help productize advanced AI capabilities.

Design, develop, maintain, and review software for critical infrastructure. Contribute to feature design, product roadmaps, Agile task breakdown, CI/CD pipelines, deployments, and customer demonstrations. Provide technical leadership by guiding engineers through delivery and technical challenges while supporting backend or frontend development across cloud, containerization, and database technologies.

Build and ship production full-stack web applications for defense and autonomy products. Own features end to end across interfaces, APIs, services, and data; establish front-end foundations and design systems; collaborate with designers and product managers; integrate field learnings into core products; and engineer secure, containerized software for demanding customer environments.