Top Python Jobs in London

Build production generative AI systems for complex financial and legal workflows. Responsibilities include document understanding, structured information extraction, model lifecycle management, retrieval and context engineering, data validation, evaluation, deployment, and monitoring. The role requires collaboration across teams, mentoring, product focus, and applying advanced AI research to convert legal and financial documents into reliable structured data.

Owns content analytics end to end, including engagement measurement, event instrumentation, audience analysis, content classification, competitive coverage tracking, and editorial attribution. Builds dbt models in BigQuery or similar warehouses and maintains the Omni semantic layer for trusted self-service reporting. Partners with editors to diagnose performance drivers, prioritize coverage, and translate complex analysis into actionable editorial strategy.

Design and maintain AWS infrastructure, Infrastructure as Code modules, CI/CD pipelines, developer portals, observability practices, and SRE capabilities. The role focuses on improving developer experience, cloud security, release velocity, reliability, and scalable engineering workflows through platform and DevOps solutions.

Owns backend systems across the product platform, leading architecture, scaling, distributed systems initiatives, production migrations, code reviews, and mentorship. Partners with Product, Data, editorial, and legal experts to deliver reliable services. The role requires strong Python, API, SQL, relational modeling, event-driven architecture, observability, and production operations experience, with opportunities to improve developer tooling and modernize legacy systems.

Lead the design, development, and deployment of scalable, production-grade machine learning systems. Establish ML engineering standards, make architectural decisions, collaborate with technical and commercial stakeholders, advise clients, and mentor junior engineers. The role requires expertise across the ML lifecycle, cloud infrastructure, software engineering, containers, orchestration, and responsible AI delivery.

Build and operate secure, scalable infrastructure for AI agents and machine learning workloads. Responsibilities include deploying AI agents, managing AWS cloud resources with Terraform and Boto3, implementing OpenTelemetry and Datadog observability, optimizing performance and costs, supporting CI/CD automation, conducting load testing and vulnerability assessments, troubleshooting platform issues, and collaborating with AI, data science, and engineering teams.

Provides technical consulting for Zendesk AI implementations, configurations, integrations, optimization, and adoption. Leads AI projects end to end, manages scope and timelines, trains stakeholders, supports change management, removes technical blockers, and aligns solutions with measurable customer outcomes. Partners with Customer Success and Sales to develop AI roadmaps, improve adoption, and support retention and expansion through analytics, success plans, and executive communication.

Lead delivery of advanced analytics and data science for global clients, develop scalable AI-enabled analytics products (prediction engines, experimentation, MMM), embed analytics into client decision-making, and build capability across teams through playbooks and thought leadership.

Lead architecture and technical strategy across multiple product teams while building and shipping full-stack features. Mentor engineers, promote AI adoption throughout the software development lifecycle, deliver LLM-backed capabilities, guide system evolution, own release processes, and support reliable continuous deployment. The role works across mobile, backend, integrations, cloud infrastructure, and production support while collaborating with Product, Design, and Engineering leadership.

Support development of C++ and Python services for fixed-income analytics, including reference data management, market-data feed integrations, bond curve configuration, and curve-fitting infrastructure. Assist with testing, code reviews, documentation, troubleshooting data quality and latency issues, and onboarding new data sources. The internship provides hands-on experience with production-grade quantitative finance systems and market data workflows.

Lead complex digital forensic and investigative engagements for clients, advising on regulatory and legal matters, conducting device/cloud forensics, directing mobile extractions and analyses, developing cloud forensics methodologies, integrating automation and AI techniques, acting as expert witness, mentoring junior staff, and collaborating across EMEA and global teams. Travel to client sites in the UK and internationally as required.

Lead delivery of advanced analytics and AI solutions for global clients, design scalable analytics products, industrialise prototypes to production, embed measurement and experimentation, drive adoption across markets, and build analytics capability and thought leadership within the organisation.

Design and build cyber ranges, CTF challenges, agentic evaluation scaffolding, and infrastructure for testing frontier AI cybersecurity capabilities. Develop metrics, interpret evaluation results, conduct pre-deployment testing, and research novel model behaviors. Collaborate with engineers, researchers, government partners, and AI labs, while communicating findings through reports, papers, and blog posts.

Build and maintain production-quality Python software for AI cybersecurity and autonomy evaluations. Responsibilities include developing cyber-range infrastructure, analysis tooling, sandbox services, image pipelines, and evaluation workflows; leading testing exercises; analysing model behavior; and communicating research findings. The role involves owning technically challenging projects, improving engineering standards, and supporting assessments that inform AI safety standards and government policy.

Own data science at a media and entertainment startup by acquiring unconventional data, analyzing cultural and audience trends, building production ML models for virality and content analysis, designing experiments, and delivering trusted dashboards and insights that guide business decisions.

Own the technical vision and architecture for a petabyte-scale data enrichment platform supporting machine-learning deployment, inference, evaluation, monitoring, annotation, and retraining. Build reliable backend services, APIs, workflows, and self-service capabilities across large GPU fleets. Lead cross-team architecture and delivery, establish platform metrics, improve reliability and cost efficiency, and mentor engineers.

Hands-on Technical Product Manager building automations and AI tools to remove manual work across sales, operations, supply chain and finance. Map workflows, prioritise opportunities, build n8n workflows, connect APIs/databases, develop LLM-based agents, use SQL and Python/JavaScript for prototypes, test with users, and embed solutions into operations.

Build production-ready AI agents and automations using n8n and LLMs to integrate systems via APIs/webhooks, use SQL and code (Python/JavaScript) to prototype, deploy and iterate autonomous workflows that reduce manual work across Sales, Operations, Supply Chain, Finance and Product.

Graduate consultants analyze datasets, build dashboards and visualizations, support data pipelines and transformations, ensure data quality and governance, and communicate insights to stakeholders. The role involves client-facing, on-site work within transformation programs, supported by structured training, mentoring, and professional accreditations. Opportunities span data analysis, business intelligence, data engineering, governance, risk and compliance, and data science.

Build and maintain production data engineering pipelines using SQL, dbt, and Python. Contribute to data ingestion, transformation, API-serving workflows, testing, debugging, cloud infrastructure, and AI-assisted engineering. Collaborate with data engineers, analysts, and product stakeholders while learning through mentorship, code reviews, and hands-on early-career training.

Lead design and implementation of secure, scalable cloud data architectures; drive adoption of AWS/Azure/GCP services; collaborate with data engineers to deliver data for analytics; assess data management technologies, promote data governance, mentor staff, and articulate architectural trade-offs to improve data quality and delivery supporting business roadmaps.

Lead the architecture and delivery of scalable batch and real-time data pipelines on GCP. Own data modeling, security, governance, reliability, performance, cost optimization, monitoring, and critical incident resolution. Translate complex business needs into robust data solutions, evaluate AI and data-engineering technologies, mentor engineers, and influence technical standards across teams.

Build and own production software across a Node.js/TypeScript backend, React frontend, C#/WPF desktop connector, and Python ML/RAG workers on Azure. Design the entity-resolution and knowledge-graph architecture, evolve APIs and service contracts, improve retrieval quality, and develop automated tests and deployment practices. The role involves direct customer interaction, technical ownership, pragmatic architecture decisions, and close collaboration with founders and structural engineering customers.

Lead engineering for an AI platform serving architecture, engineering, and construction firms. Own technical direction, platform architecture, knowledge graph and entity-resolution systems, engineering standards, delivery, hiring, and team development. Remain hands-on while building a small engineering function, collaborating directly with customers and product leadership. The platform includes Node.js/TypeScript, React, C#/WPF, Python ML/RAG workers, Azure, distributed systems, and event-driven architecture.

Graduate data engineering role focused on building and maintaining SQL/dbt transformation pipelines, Python ingestion services, API data pipelines, and data quality processes. The role includes production coding, testing, debugging, code reviews, cloud infrastructure exposure, AI-assisted engineering, and collaboration with engineers, analysts, and product stakeholders. It is designed for recent graduates with foundational SQL and Python experience and offers mentorship, training, and cross-functional development opportunities.