Top Python Jobs in London

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.

Build and maintain scalable backend systems using Java, Node.js, Spring, AWS, microservices, and REST APIs. Lead architecture and technical design, promote secure and reliable engineering practices, drive automated testing and CI/CD, mentor engineers, collaborate with clients and cross-functional teams, and evaluate AI and emerging technologies to improve software delivery.

Develop, enhance, and validate quantitative risk models for derivatives and other financial products. Implement risk analytics solutions on the Atoti platform, analyze pricing and valuation methodologies, investigate exposures and hedging strategies, and collaborate with clients, quants, risk managers, and engineering teams. Translate quantitative requirements into scalable technology solutions while supporting client workshops, product innovation, and risk management analytics across multiple asset classes.

Lead mobile security architecture, penetration testing, threat modeling, security assessments, and secure solution delivery across Android and iOS platforms. Define technical strategies, identify risks, recommend mitigations, advise clients, and drive security quality and compliance. Collaborate across full-stack mobile teams, improve security practices and tooling, and support Agile delivery. The role also involves mobile security research, reverse engineering, cryptographic controls, and DevSecOps practices.

Ten-week Internal Audit internship supporting risk-based assurance, governance, risk management, and control-environment reviews within a global investment manager. Interns contribute to live projects, analyze data for trends and anomalies, prepare high-quality documentation, collaborate with stakeholders, and learn from experienced audit professionals. The programme includes training, shadowing, networking, and potential progression to a graduate programme.

Twelve-month internal audit placement supporting live projects, research, data analysis, assurance activities, and strategic objectives within a global investment manager. The role involves analyzing trends and anomalies, contributing to risk-based assurance, collaborating with stakeholders, maintaining confidentiality and ethical standards, and developing professional knowledge through training, mentoring, and networking. Candidates must be available for the full programme beginning June 14, 2027.

Analyze and model electricity grids across global markets, including power flows, grid constraints, interconnection queues, stability, and techno-economic optimization. Support grid investment plans, capex strategy, and non-wire solutions such as flexibility and battery energy storage. Develop data and modeling tools, generate insights from complex energy datasets, and communicate findings clearly to technical and non-technical stakeholders.

Own electrical engineering and project delivery for energy infrastructure serving data centres and industrial customers. Responsibilities include managing development programmes and budgets, grid connection processes, electrical design, consultant and contractor coordination, design reviews, risk management, procurement, commissioning, energisation and handover. The role requires MV/HV infrastructure experience, GB or European grid connection expertise, multidisciplinary design coordination and accountability across first-of-a-kind power projects.

Maintain the Linux desktop platform, including OS image build pipelines, provisioning tooling, security, monitoring, hardware lifecycle, and system health. Troubleshoot bare-metal and virtual machine provisioning across OpenStack and Ironic, support technical users, manage incidents and changes, and contribute to capacity planning. The role requires strong Linux administration, Python scripting, infrastructure automation, security, diagnostics, and communication skills.

Lead the design and operation of IAM technologies, integrating with cloud platforms, supporting audits, and developing a high-performing engineering team.

Prepare daily P&L and market risk reports for oil, refined products, and biofuels trading; monitor limits, identify significant risks, and support senior management. Develop forward curve methodologies, assess new products, review Endur changes, and support month-end activities. Collaborate with traders, front office, back office, and risk committees while mentoring junior team members and contributing to automation and AI-enabled trading tools.

Designs, develops, and supports secure cloud-based data integration solutions using Microsoft Fabric and Azure. Responsibilities include client discovery, requirements gathering, ETL and ELT development, REST API and SFTP integrations, data pipeline implementation, production monitoring, incident investigation, and reliability improvements. The role collaborates with architects, analysts, project managers, and delivery teams across multiple client engagements.

Design, build, evaluate, debug, and deploy AI systems for quantitative investment and trading firms. Develop prototypes, integrations, evaluation harnesses, production accelerators, and reusable architectures. Partner with quant researchers, engineers, executives, and security teams to address reliability, latency, cost, safety, governance, and adoption. Lead technical workshops, diagnose implementation issues, and translate customer feedback into product requirements.

Designs and deploys production computer vision solutions using Ultralytics YOLO. Responsibilities include technical discovery, architecture design, Python prototyping, model training and optimization, benchmarking, deployment across cloud and edge environments, customer demos, workshops, proof-of-concepts, stakeholder management, and creating reusable documentation and integrations. The role partners with Sales, Customer Success, Product, and Engineering while owning technical outcomes throughout the customer lifecycle.

Manages Data Science Leads across multiple sports modeling teams, ensuring consistent technical standards, delivery quality, resource planning, and team development. The role supports forecasting model delivery, hiring, performance management, cross-functional planning, capacity management, and department-wide strategy. It also serves as an escalation point for technical and resourcing challenges while fostering collaboration, innovation, and knowledge sharing across the data science organization.

Build and maintain scalable backend systems, APIs, and partner integrations for embedded lending flows. Own projects from scoping through production, collaborate with engineers and stakeholders, improve customer conversion through experiments, and use data to guide product decisions. The role emphasizes robust system design, clear documentation, pragmatic delivery, AI-assisted development, and measurable impact for partners and small-business customers.

Execute and manage repo, reverse repo, and secured financing transactions across government bond and credit markets. Optimize portfolio financing by analyzing repo pricing, collateral dynamics, dealer balance-sheet capacity, and market structure. Build relationships with dealers, portfolio managers, analysts, and traders; identify financing opportunities; manage liquidity, counterparty, settlement, and operational risks; and improve processes through technology, data, electronic trading, central clearing, and collateral optimization.

Design, build, deploy, and support agentic AI and intelligent automation solutions across enterprise platforms. The role develops autonomous workflows, orchestration, RAG pipelines, prompts, integrations, guardrails, human oversight, monitoring, and auditability. It also establishes AI engineering standards and supports production reliability, incident resolution, governance, and continuous improvement while communicating technical designs and risks to diverse stakeholders.

Design and build backend systems for AI agent orchestration, tool execution, workflow management, memory, and state handling. Develop scalable APIs and reliable infrastructure for distributed AI workflows, improve platform reliability and observability, and partner with product, research, and engineering teams to deliver agent capabilities. Own technical initiatives from architecture through production, maintain software quality through testing and continuous delivery, and mentor engineers on backend and distributed-systems practices.

Build and operate production software, APIs, tooling, and automation for large-scale GPU, bare-metal, and HPC infrastructure. Responsibilities include provisioning, configuration, monitoring, lifecycle management, observability, hardware integration, reliability improvements, and infrastructure capacity deployment. The role partners with networking, data center, platform, and infrastructure teams to design scalable systems and define technical direction.

Build, validate, and operate large-scale bare-metal GPU infrastructure for AI/ML and HPC workloads. Responsibilities include managing Linux systems, image pipelines, test clusters, provisioning, firmware and driver validation, GPU diagnostics, performance analysis with NVIDIA DCGM, automation, virtualization, and hardware management interfaces. The role requires troubleshooting across hardware and software layers while collaborating with infrastructure, hardware, data center, platform, and ML teams.

Operate, scale, and optimize distributed storage infrastructure supporting large-scale AI/ML and HPC workloads. Build Python automation, manage Linux bare-metal systems, troubleshoot storage, hardware, networking, and operating system issues, and improve performance, reliability, monitoring, capacity planning, and lifecycle management. Collaborate with infrastructure, networking, platform, and data center teams on storage deployments and scaling strategies.

Design and scale backend systems for AI agent orchestration, distributed workflows, experiment management, platform APIs, and developer tooling. Build reliable cloud-native infrastructure, improve observability and performance, partner across engineering and research teams, maintain software quality through testing and continuous delivery, and mentor engineers on distributed systems and backend best practices.

Develop and scale the Lightning AI platform across frontend, CLI, APIs, and backend systems. Build features using React, Python, or Go; improve stability, performance, architecture, automation, and continuous delivery. Collaborate with engineering, product, and design leaders, own end-to-end feature development, reduce technical debt, and mentor engineers on system design and problem-solving.

Build AI agents, reusable agent capabilities, and agentic workflows that automate software development at Preply. Develop integrations with codebases, APIs, MCPs, internal tools, and engineering systems; design orchestration, context, evaluation, verification, and human-in-the-loop mechanisms; and take prototypes into reliable, scalable solutions. Collaborate across engineering, product, design, and data teams while measuring adoption and business value.