Top Python Jobs in London

The Senior Data Modelling Consultant will design enterprise-grade data models, support data migrations, and enforce data governance standards for financial services clients.

Deploy and configure cybersecurity platforms for customers across Google SecOps and Microsoft Sentinel. Responsibilities include onboarding log sources, configuring connectors and ingestion pipelines, deploying SIEM/SOAR content, troubleshooting technical issues, training customers, documenting configurations, and managing multiple concurrent deployments.

Evaluate AI coding-agent interactions for quality, engineering judgment, reasoning, explanations, and user trust. Provide clear written feedback, distinguish response-quality levels, help define evaluation standards, and record short video explanations. The role requires senior-level software engineering experience, strong TypeScript/JavaScript or Python skills, familiarity with OpenAI Codex, Claude Code, or Cursor, and excellent English communication.

Owns end-to-end QA coverage across firmware, cloud services, and mobile applications for IoT products. Tests device-to-cloud communication, APIs, business logic, connectivity failures, offline operation, OTA updates, and rollback scenarios. Analyzes device and cloud logs, develops test data strategies, prioritizes automation scenarios, and maintains smoke and regression testing processes. Requires systems thinking, embedded and mobile testing experience, API testing, Agile QA processes, and familiarity with relevant test management tools.

Manage ESG datasets throughout ingestion, validation, quality review, documentation, governance, and delivery for equity and fixed income indices. Design data quality checks, investigate issues, define feed requirements, support dataset onboarding, maintain lineage and controls, coordinate with vendors and internal teams, and provide ESG data analysis and stakeholder support.

Analyze marketing performance, customer behavior, cohorts, retention, profitability, and lifetime value to identify growth opportunities. Build dashboards, segmentation models, business cases, revenue scenarios, and market analyses supporting advertising, product development, category expansion, acquisitions, and strategic decisions. Present actionable insights directly to founders and leadership while improving analytics and reporting systems.

Lead end-to-end machine learning systems across data, training, fine-tuning, evaluation, inference, deployment, and monitoring. Build scalable GPU-based infrastructure and reliable production pipelines for large models, optimize performance and cost, and partner with research and application engineering to translate model capabilities into product improvements. Provide hands-on technical leadership, resolve production issues, and ensure measurable, safe, and maintainable ML iterations.

Lead the execution of production ML systems: build data pipelines, training workflows, evaluation and inference infrastructure, fine-tune large models (LoRA/QLoRA/SFT/DPO), optimize GPU deployments, and integrate ML into products while ensuring robustness, safety, and reliable user-facing performance.

Lead implementation of production-grade ML systems: data pipelines, training/evaluation workflows, scalable GPU inference, model fine-tuning (LoRA/QLoRA/SFT/DPO), deployment, and monitoring. Collaborate with product and application teams to ship reliable, efficient, and safe ML-powered features for real users while managing latency, cost, and robustness constraints.

Support Cboe’s mission-critical market data and analytics platforms by investigating complex issues, analyzing logs and data, managing escalated incidents, coordinating post-incident reviews, and driving root-cause fixes. Responsibilities include customer and business support, system health checks, SQL and UNIX diagnostics, runbook creation, technical documentation, and mentoring Tier 1 staff. The role requires market knowledge, FIX protocol familiarity, production support experience, and comfort with Linux, Windows, SQL, and AI tools.

Lead customer engagements building and productionizing AI and generative AI applications on Databricks. Responsibilities include architecture, technical delivery, client advisory, team leadership, workshops, MLOps, production pipelines, stakeholder communication, and technical enablement. The role requires strong Python, cloud, MLflow, Databricks, PySpark, SQL, Delta Lake, governance, LLM, and modern AI framework expertise in a consulting environment.

Leads the global engineering organization across London and the US, owning technical strategy, engineering operations, reliability, delivery processes, culture, and team performance. Manages Tech Leads and develops engineering leadership capability while partnering with Product, Design, People, GTM, and enterprise customers. Establishes standards, metrics, incident response, continuous deployment, and scalable ways of working across a distributed SaaS engineering organization.

Own and evolve Chip’s data platform architecture, including scalable pipelines, medallion architecture, data modeling, governance, security, and self-service enablement. Establish engineering standards, apply Databricks and Terraform best practices, optimize performance, and mentor data engineers. The role requires hands-on expertise in Databricks, Python, PySpark, SQL, databases, Git, infrastructure as code, and PII governance, with opportunities to re-architect the platform in a regulated fintech environment.

Lead design, build, and maintain large-scale, secure data engineering solutions. Produce and review production code, improve operational stability, drive AI-assisted engineering adoption, evaluate vendors, and shape technical strategy and team culture.

Build and maintain Python services, data models, pipelines, and APIs that transform satellite and ground-truth data into environmental intelligence. Own technical decisions, prototype and ship customer-facing capabilities, extend existing systems, and ensure reliability through maintenance and documentation. Collaborate with Product, Customer Solutions, Science/AI, and enterprise customers while mentoring engineers, improving tooling, and demonstrating solutions commercially.

Build and maintain end-to-end software across user interfaces, APIs, data pipelines, and platform systems. Develop MVPs and proof-of-concepts, make architectural decisions within existing constraints, and support maintenance, documentation, and operational reliability. Contribute through code reviews, mentoring, tooling improvements, demos, and cross-functional collaboration. The role requires independent judgment, rapid iteration, and practical use of AI in a fast-moving startup environment.

Designs and delivers Oracle-centric multi-cloud architectures across OCI, Azure, AWS, and GCP. Responsibilities include solution architecture, landing zones, Oracle Database and EBS administration, data centre exits, cloud migrations, Linux infrastructure, automation, CI/CD, monitoring, security, and technical governance. The role produces HLDs and LLDs, leads design reviews and stakeholder workshops, manages technical risk, supports pre-sales, and guides migration planning, execution, and validation.

Supports ML infrastructure for autonomous driving by maintaining large multimodal datasets and feature data, improving workflows, and identifying user and agent use cases. The intern develops systems for machine learning training and inference, using Python or C++ and distributed systems. Preferred experience includes feature extraction, distributed storage, TensorFlow, JAX, deep learning, autonomous driving, or robotics. The role is hybrid and requires current Master's or PhD enrollment.

Operate and enhance a global trade surveillance program across multiple asset classes. Triage alerts, investigate potential market abuse, calibrate scenarios, reduce false positives, identify control gaps, and support regulatory reporting. Partner with Compliance, Trading, Operations, Legal, and technology teams to improve surveillance systems, automate monitoring, manage exceptions, prepare regulatory responses, and implement compliance policies. Monitor position limits and disclosures while producing accurate management information and reporting.

Provide hands-on L1 technical support for macOS and Windows endpoints, identity platforms, SaaS applications, and business systems. Manage tickets, troubleshoot access and configuration issues, coordinate escalations, and support onboarding and offboarding. Maintain device compliance, patching, MDM configurations, and physical access systems. Contribute to incident analysis, automation, knowledge-base documentation, and improved self-service while delivering strong response times and customer satisfaction.

Develop and improve backend pricing, discounting, subscription, payment, and financial reporting systems for a large-scale electric vehicle charging platform. Monitor and debug applications and infrastructure, maintain asynchronous processing systems, support payment issue resolution, and collaborate with cross-functional teams to deliver robust, scalable software.

Build governed data assets, reliable pipelines, lakehouse models, and analytical products using Microsoft Fabric and Databricks. Conduct exploratory analysis and develop validated statistical and machine-learning models for classification, ranking, recommendation, forecasting, anomaly detection, NLP, entity resolution, and information extraction. Implement data quality, lineage, monitoring, testing, documentation, and responsible AI practices while supporting legal-services R&D, client intelligence, opportunity identification, pricing, and matter analysis.

Build and deploy production AI products for legal services, including agentic workflows, retrieval, evaluation harnesses, observability, APIs, integrations, and user interfaces. Develop maintainable Python and TypeScript code, implement secure enterprise patterns, and take AI features from prototyping through controlled production. Measure and improve quality, reliability, latency, and cost through testing, tracing, monitoring, and user feedback. Collaborate across product, data, technology, security, and multidisciplinary R&D teams.

Build and operate frontend-led full-stack products for legal R&D, using TypeScript, React, Next.js, backend APIs, Azure services, and AI integrations. Own frontend architecture, accessible user experiences, reusable component libraries, authentication, enterprise integrations, deployment, monitoring, testing, security, and continuous improvement. Collaborate with AI engineers, data engineers, data scientists, designers, lawyers, and business stakeholders to deliver document-heavy, search, workflow, and AI-assisted applications.

Own engineering quality across the organization through quality gates, SLOs, incident follow-ups, risk reporting, performance testing, and shift-left practices. Lead AI adoption in testing, contribute to test frameworks and CI pipelines, and hire and manage Quality Engineers embedded across teams. Partner with senior stakeholders to drive quality initiatives, improve reliability, and establish a company-wide quality mindset for large-scale B2C products.