Top Python Jobs in London

Develop, verify and calibrate Infoworks ICM wastewater and urban drainage models; perform hydrological assessments; advise clients; support tenders and business development; promote SuDS and sustainable solutions; collaborate across UK, Ireland and France teams.

Hands-on Senior Platform Engineer to design, build, and maintain multi-cloud (AWS/GCP/Azure) platform infrastructure. Responsibilities include IaC (Terraform/Ansible), CI/CD and DevSecOps, Kubernetes (EKS/AKS/GKE) administration, observability, and developer self-service tooling. Mentor junior engineers, drive platform standards, and collaborate across engineering, security, and FinOps to ensure secure, reliable, and cost-effective platform delivery.

Lead architecture and engineering of account compromise detection, response, and remediation for Claude. Scope and deliver complex, adversarial-resilient detection systems, run incident investigations, design automated response flows, threat-model attacker behavior, drive cross-team alignment, set technical standards, and convert investigations into durable automated defenses.

Design, build and maintain high-quality, scalable margin and collateral systems. Collaborate with product and global teams, implement RESTful APIs, apply TDD and clean code, and solve complex engineering challenges in a fast-paced finance environment.

Front-office quant team role designing, implementing, testing and deploying high-performance valuation models and pricers for derivatives. Collaborate with trading desks, engineering, and risk to digitalize derivatives business, optimize performance, deploy models across platforms, support daily trading floor activities, and drive technical innovation.

Support building and maintaining a modern data lakehouse platform: implement storage standards, develop ingestion and transformation pipelines, capture lineage, enforce data quality, collaborate with analytics and business teams, follow governance/security practices, participate in agile delivery, and troubleshoot issues with senior engineers.

This role involves collecting data for model training, operating cloud infrastructure, and collaborating with scientists to enhance dataset quality and efficiency.

Perform product and behavioural analysis (funnel, cohort, segmentation), define KPIs, run A/B tests, build dashboards, partner with Product and Analytics Engineering, and apply AI/ML techniques to drive product decisions and experimentation.

Lead and grow a team of ML engineers to build and operate production ML and LLM systems—real-time inference APIs, batch data products, and agentic AI—ensuring deployment, monitoring, CI/CD, reproducibility, and measurable business impact.

Own end-to-end delivery of production ML and LLM-powered systems: design and build training/deployment pipelines, package and deploy models, implement monitoring for performance and drift, support batch and real-time workloads, evolve internal ML platform and tooling, promote best practices, and provide technical leadership and mentoring across data science and engineering teams.

Lead design and deployment of ML models and pipelines for the AI platform. Translate business problems into measurable ML solutions, design experiments and A/B tests, and own model performance, monitoring, degradation detection and retraining. Champion responsible, explainable AI in a regulated financial context, choose between classical ML, LLMs and agentic approaches, collaborate with ML engineering, product and commercial teams, and mentor junior data scientists.

Lead high‑impact AI and decisioning projects from discovery through production. Define technical roadmaps, design and validate models (including causal/experimentation), partner with engineering to productionise services with monitoring and governance, mentor data scientists, and promote responsible AI practices and platform adoption.

Partner with cross-functional product teams to analyse large behavioural datasets, define product metrics, design and evaluate A/B tests, build measurement frameworks, and deliver insights via visualisations to influence product roadmaps and drive commercial outcomes.

Build, deploy, and maintain production ML and AI systems: model packaging, lifecycle automation, monitoring, batch and real-time workloads, and platform improvements while collaborating with data scientists, engineers, and product teams.

Lead a data science team building always-on predictive models and pipelines for an AI platform. Own model lifecycle: production deployment, monitoring, drift detection, retraining, and measurable business outcomes. Provide technical leadership, code reviews, architecture decisions, and stakeholder collaboration. Hire and mentor data scientists, champion responsible and auditable AI, and adopt ML platform tooling (MLflow, model registries, Databricks) and LLM/RAG approaches where appropriate.

Design, build, and deploy machine learning solutions to improve customer journeys, pricing, and personalization. Perform feature engineering, statistical analysis, A/B test design and evaluation, collaborate with cross-functional teams, and support model deployment, monitoring, and responsible AI practices. Mentor juniors and contribute to team standards and knowledge sharing.

Design, build, operate and optimise scalable data pipelines and platform components. Deliver production-ready solutions, support production incidents, improve data quality, and collaborate across engineering and analytics teams.

Design, build, and maintain scalable, fault-tolerant serving platforms for LLMs. Integrate models for production and research, implement monitoring and observability, perform architecture reviews, and lead cross-functional end-to-end projects enabling LLM capability discovery and high-performance serving.

As a Senior Data Scientist, you will build and maintain production models for pricing decisions, define pricing dynamics, and measure strategy impact within a collaborative team.

Lead the Trading Anti‑Fraud team to detect, investigate, and prevent trading fraud across products and regions. Enhance monitoring rules, manage alerts and cases, collaborate with Trading/Risk/Product/Compliance, and provide strategic reporting and recommendations to senior leadership to reduce fraud losses and improve controls.

Build, own, and maintain internal benchmark suites for single- and multi-turn content and agentic guardrails; create benchmarks distinguishing model capabilities; extend evals to new verticals and product features; collaborate on research quantifying realistic LLM and agent failure modes; support product integration of core model functionality.

As a Software Engineer at Wexler, you'll architect and build full-stack applications, develop features, collaborate with AI engineers, and contribute to technical decisions while working in a fast-paced startup environment.

Act as a technical customer-facing lead to drive adoption of Veracode solutions: onboard customers, integrate products across CI/CD toolchains, automate via APIs, resolve complex app/cloud security issues, present to stakeholders, and feed customer insights to product teams.

Hybrid Business Systems Analyst role supporting Treasury Analytics and Production Support. Responsibilities include eliciting business requirements, implementing Azure/Spark solutions, writing Spark SQL, supporting FIS Quantum and TBSM, L1/L2 incident management, change deployments, troubleshooting, process improvement, and documentation within ITIL/Agile frameworks.

Lead the evolution of Tenable's multilingual delivery platform toward AI/LLM-led localization. Design event-driven, API-first translation pipelines, integrate LLMs into production workflows, extend AI-led delivery across content types, define standards and governance, build AI-powered reporting, and deliver key initiatives while coordinating distributed and contracted engineering partners.