Top Python Jobs in London

This role involves analyzing complex datasets, developing metrics, building dashboards, and collaborating with teams to enhance fraud detection strategies using ML models.

Set and drive front-end technical strategy across multiple teams for a complex merchant platform. Architect scalable, performant, accessible front-end systems (component system, state management), shepherd Primer AI Companion integration, mentor engineers, ensure observability and cross-team coherence, and partner with Product, Design, and backend teams.

This role involves leading the development of AI systems to replace lab toxicity experiments, focusing on end-to-end ML architecture, research on chemistry-biology correlations, and building innovative models. Candidates should have strong ML skills, engineering abilities in Pytorch and Python, and a passion for both science and technology.

The L3 SOC Analyst leads complex incident investigations, drives automation, mentors junior analysts, and enhances SOC capabilities using advanced security technologies.

Design and validate agentic AI capabilities for Protege in PatentSight using ML, NLP, and LLMs. Build retrieval and hybrid search strategies, run experiments, evaluate models, analyze large IP datasets, and translate results into product recommendations while collaborating with engineers, product, and IP experts.

Design, experiment, and validate NLP/LLM and generative AI solutions for patent intelligence. Develop agentic workflows, retrieval strategies (hybrid semantic+lexical), evaluation metrics, and large-scale text processing methods. Partner with product and engineering to translate validated models into scalable, customer-facing features and deliver measurable business impact.

The Director of Science will guide scientific research in genetics, leading algorithm development and managing a team to enhance MyHeritage's DNA products, impacting user ancestry discovery.

The Data Engineer will build and scale data pipelines, ensure data quality, implement dbt best practices, and empower cross-functional teams with self-service data access.

The Senior Platform Engineer will enhance reliability, performance, and scalability of systems, improve operational excellence, and automate processes. Responsibilities include incident management, observability solutions, cost optimization, and cross-functional collaboration.

The Sustaining Operations Engineer resolves complex Linux-related issues, collaborates with teams, participates in communities, and provides escalation support, driving issues to resolution and maintaining operational success.

As a Software Engineer - L3 Support, you will resolve complex Ubuntu-related issues, collaborate with teams, and participate in upstream communities to drive bug fixes and improvements.

As a Software Engineer, you will resolve complex customer issues related to Ubuntu and open source software, participate in upstream communities, and develop and review code across multiple layers of the software stack.

As a Software Support Engineer, you will troubleshoot and resolve complex issues related to Ubuntu and open source technologies, collaborate with teams, and contribute to community development.

Canonical is seeking exceptional software engineers for the Linux kernel team, involving collaboration on Ubuntu's kernel development across various hardware. Responsibilities include diagnosing kernel issues, improving automation, and participating in global tech events twice a year.

The Senior Application Developer will create applications on D-Wave's quantum computers, collaborate with stakeholders, and solve complex problems using algorithms in Python.

The Senior Sales Engineer at MariaDB will manage technical sales processes, design solutions for clients, and collaborate with sales to close deals in the EMEA region.

Monitor and analyze customer vulnerability and asset risk data using Qualys TruRisk; research threats and exploits; produce reports and briefings; track remediation; onboard environments; escalate high-risk findings; improve playbooks and processes.

Lead teams to design, architect, provision, migrate, operate and govern hybrid cloud and on-premise infrastructure. Deliver strategies, automation, security, resilience and lifecycle management for enterprise-scale transformations.

Lead design and delivery of AI-ready infrastructure across on-prem, colocation and cloud. Define GPU compute architectures, ensure secure scalable GPU platforms for training, fine-tuning and inference, and manage distributed architecture and engineering teams during hybrid cloud transformations.

Design, build, migrate, operate and govern hybrid cloud and on-prem infrastructure. Architect and deliver cloud transformations, implement virtualization, container platforms, automation, and resilience strategies.

Design, implement, and optimize enterprise LAN/WAN/WiFi and cloud network architectures. Lead cloud networking (AWS/Azure/GCP), automation (Ansible, Python, Terraform), monitoring, troubleshooting, and enforce security/compliance. Collaborate with cross-functional teams and mentor junior engineers.

Ingest, transform, harmonise and quality-control large genomic datasets (GWAS, genotypes, phenotypes). Configure pipelines, interpret QC diagnostics, investigate anomalies, curate metadata, and collaborate with engineering and science teams to expand genomic data resources and schemas.

Design, build, test and maintain enterprise applications using Microsoft Power Platform and related technologies. Lead design and code reviews, gather and prioritise stakeholder requirements, support system integrations, plan releases, troubleshoot production issues, and promote quality, security and continuous improvement across the product team.

Responsible for deploying, operating, and ensuring reliability of a centralized AI-SDLC platform. Build/manage infrastructure, CI/CD pipelines, observability, security/compliance controls, and provide operational/on-call support to meet enterprise SLAs while optimizing cost and scalability.

Build and maintain production data pipelines and systems in Python; lead technical direction for the Insights platform; improve engineering standards, observability, and data quality; apply AI across products and pipelines; help develop AI-ready data foundations such as semantic layers.