Top Python Jobs in London

Lead market risk oversight for an energy trading desk across gas, power and renewables. Quantitatively analyse deals, improve VaR, stress testing and option pricing methodologies, support digitisation, and build relationships with front office while managing junior staff and integrating risk into new products and processes.

Lead identification, design, and implementation of automation and reporting across Controllership. Build Python solutions for data collection, reconciliation and controls; develop Power BI/Tableau dashboards; support Microsoft Fabric and evaluate AI/Copilot use cases. Partner with stakeholders, drive continuous improvement, document solutions, and provide training to increase automation and operational transparency.

Lead and own end-to-end development of revenue-critical full-stack systems: define problems, design architectures, make pragmatic trade-offs, deploy and ensure production reliability, and translate technical constraints into business impact. Work across backend, frontend, and infra, mentoring teams and using AI-assisted tools to accelerate delivery.

Build and own end-to-end full‑stack features: implement, test, deploy, and maintain production code. Collaborate in pair programming and code reviews, use modern tooling (including AI-assisted tools), and prioritize product-focused solutions with clean, maintainable implementation.

Partner with sales and channel teams to present Snowflake, run demos, proofs-of-concept, and design implementations; build technical champions, articulate data architecture and cloud solutions, and collaborate with product, engineering, and partners to drive deals and customer success.

Lead technical pre-sales efforts for UK accounts: present Snowflake to technical and executive audiences, build demos/POCs, develop technical champions, collaborate with sales, partners, product and engineering, and represent Snowflake at events to drive deal wins and implementations.

Lead the design, build, and productionisation of Fundment's AI systems and infrastructure on GCP. Define AI strategy, architect LLM/RAG/agent solutions, implement MLOps, model governance, IaC, monitoring, cost optimisation, and mentor a multidisciplinary team to embed AI into products and operations.

Work with quantitative researchers and portfolio managers to build, maintain and productionise quantitative trading frameworks and strategies, develop tooling and ML pipelines, improve engineering standards, and deliver robust production solutions for systematic trading.

Build and own cloud infrastructure and GPU compute environments for ML model training and deployment. Implement IaC, CI/CD, orchestration, containerisation, monitoring, security, and internal tooling to enable reproducible, scalable research-to-production ML workflows.

Develop, train, and fine-tune machine-learned interatomic potentials (MLIPs) for magnetic and structural materials. Design DFT dataset workflows, active learning loops, and extend MLIP architectures to capture spin-lattice interactions. Build reproducible model and dataset pipelines and collaborate with materials scientists to translate physical intuition into training objectives.

Implement and scale cutting-edge ML research into production-ready systems: build distributed training and inference on GPU clusters, optimize model performance, create evaluation and experiment-tracking tooling, architect multimodal scientific data pipelines, and collaborate with researchers and materials scientists to deploy models in closed-loop hardware environments.

Build and maintain software and orchestration that turns a physical lab into an autonomous system: implement instrument drivers, coordinate robots and data pipelines, automate workflows, integrate new instruments, and ensure reliability, error handling, and reproducibility while collaborating with materials scientists and ML researchers.

Design and own data architecture and ingestion pipelines that convert wet-lab instrument outputs into versioned, reproducible datasets for ML and RL training. Build tooling for dataset inspection, lineage, and integration with model training workflows, and collaborate closely with ML researchers to support pretraining, midtraining, and RL experiments.

Develop and implement advanced theoretical and computational methods beyond standard DFT to predict intrinsic magnetic properties. Apply GW/DMFT and Green's-function many-body techniques, derive analytic formalisms, implement numerically, integrate with DFT production workflows, and collaborate with ML teams to improve predictive models and training data.

Lead ML research for an AI-driven materials discovery platform: design and prototype architectures (GNNs, generative, foundation models), develop active learning/optimization strategies, address representation learning for sparse noisy experimental data, collaborate with materials scientists to encode physics into models, and drive proofs-of-concept to production with engineering partners.

Lead technical vision and target-state architecture for large-scale, mission-critical enterprise systems. Design resilient hybrid/multi-cloud (Azure/AWS) data and AI architectures, champion governance/security, drive ARB processes, mentor senior engineers, own AI/ML integration and MLOps, and deliver hands-on prototypes and PoCs to ensure scalable, compliant production solutions.

Build, operate, and automate highly available, scalable cloud-native services. Design DevOps tools, implement CI/CD/GitOps pipelines, provision infrastructure with Terraform, manage large-scale Docker/Kubernetes environments, and develop monitoring/automation for distributed systems.

Conduct original alpha research on global macro assets, building and testing predictive signals (trend timing and directional). Model dynamics across rates, FX, equities and commodities, evaluate signals statistically, contribute to portfolio construction and risk analysis, monitor live strategy performance, and collaborate with researchers and portfolio managers to implement and refine trading strategies.

Run middle-office operational processes including NAV estimation, valuations, reconciliations, and P&L review. Validate fund administrator NAVs, maintain capital and portfolio records, support reporting and analytics, and help automate and improve processes while developing technical and market knowledge.

Support growth of non-ferrous metals trading by building and owning fundamental supply/demand models (initial focus on Copper, expanding to Aluminium). Produce forecasts, price outlooks, and market intelligence; maintain proprietary databases; use Python, SQL and analytical/AI tools; present insights to traders and senior management; collaborate with Trading, Risk and Operations.

Build and scale AI-driven backend systems and infrastructure for retail intelligence: develop the AI chatbot, agents, tooling and MCP integrations, evolve data processing across AWS and bare metal, own features end-to-end, and mentor team members.

Build and maintain shared platform tools and infrastructure to help squads ship faster and more reliably. Work both embedded with domain teams and on central platform projects: CI/CD, feature flags, canary rollouts, AWS infrastructure, observability, automation, AI integrations, and production support using metrics, logs, and SLOs.

Contribute to marketing effectiveness and data science projects: clean and prepare data, build and apply MMM and predictive models, create visualizations and dashboards, support client meetings, and deliver actionable media optimization recommendations.

Platform Engineer embedding across frontend, backend, data and warehouse teams to build shared infrastructure, CI/CD, feature flags and observability. Integrate AI across the engineering lifecycle, maintain core AWS/GCP services, automate toil, coach squads on SLOs and ownership, and proactively scope and deliver high-impact platform work.

Lead product-focused analytics and experimentation to uncover growth opportunities, define KPIs, analyze user behavior and retention, quantify feature impact, and communicate data-driven recommendations to cross-functional stakeholders.