Top Python Jobs in London

Own regulatory analytics and reporting by building audit-ready datasets, pipelines, dashboards, compliance metrics, and automated workflows. Analyze complex data, monitor quality, support audits and regulatory exams, and deliver actionable insights to compliance teams and leadership. Translate regulatory requirements into accurate reports using Python, SQL, dbt, and Airflow while partnering cross-functionally to manage data issues and remediation.

As a Software Engineer at Citadel, you will build systems for investments, collaborate with researchers, and tackle complex problems.

Build and maintain data pipelines and integrations to enable continuous control monitoring for security and compliance. Translate control requirements into measurable KPIs and dashboards, partner with control owners, and deliver data-driven insights to reduce risk and improve compliance processes.

Manage and optimize business-critical PostgreSQL databases and cloud-based SaaS infrastructure. Responsibilities include installation, configuration, performance tuning, backup and recovery, replication, high availability, disaster recovery, security, patching, upgrades, migrations, SaaS administration, SSO, IAM, monitoring, automation, incident response, Linux and cloud administration, compliance, and continuous improvement.

Designs and operates secure, scalable AWS infrastructure and reusable Terraform modules. Owns CI/CD, GitOps, observability, reliability, automation, and cost optimization. Builds production platforms for AI agents and LLM-backed services, including identity, access controls, secrets, evaluation, regression testing, and operational safeguards. Mentors engineers, communicates technical trade-offs, and supports broader platform and developer-experience strategy.

Build and optimize ZOE’s customer-facing website using TypeScript, React, and Next.js, with a strong focus on accessibility, performance, responsive design, and design-system development. Collaborate with designers, analysts, and product teams on user experiences and A/B testing. Contribute to backend services using Kotlin or Python, support API development, write maintainable code, and ship production improvements regularly.

Solve ambiguous operational and risk problems through data analysis, workflow design, dashboards, monitoring, alerts, and lightweight automation. Analyze complex datasets, validate pipelines and safety signals, investigate anomalies, design quality controls, and support emerging-risk detection systems. Use AI tools with testing and human oversight, communicate findings and tradeoffs, and coordinate cross-functional handoffs across Operations, Product, Engineering, Data Science, Safety, Legal, and Support.

Design, deliver, engineer, govern, and support AI applications, agents, BI solutions, and data integrations. Collaborate with stakeholders to define requirements, create technical designs and test plans, manage third-party delivery, evaluate emerging technologies, and ensure solutions meet architecture, compliance, supportability, and lifecycle standards. Use AI-assisted development and Azure services to simplify processes, improve workflows, and reduce support overhead.

Designs, deploys, migrates, supports, and troubleshoots complex enterprise and data center networks. The role focuses on Cisco ACI and ISE, firewalls, routing, Layer 2/3 technologies, technical documentation, customer workshops, solution design, and deployment delivery. It also supports pre-sales, mentoring, and development of next-generation AI networking expertise.

Develop and optimize GPU clusters and InfiniBand networks for hyperscale HPC environments. Responsibilities include troubleshooting hardware and infrastructure issues, integrating new GPUs through Kubernetes, QEMU, and KVM, managing GPU devices and InfiniBand fabrics, tuning performance, and improving automated monitoring and fault resolution.

Own and evolve Wave’s observability platform across backend services, APIs, databases, Kubernetes, cloud, and on-premises systems. Define SLOs, improve alert quality, build internal tooling, support instrumentation, identify reliability and performance issues, and help engineers investigate incidents. The role includes operating observability tools, establishing standards, configuring OpenTelemetry at scale, and controlling platform costs while partnering with infrastructure, database, security, and product engineering teams.

Build scalable performance tooling for AI model training and inference across cloud and embedded hardware. Responsibilities include profiling workloads, conducting roofline analysis, identifying layer- and operator-level bottlenecks, predicting latency, memory, utilization, and compute costs, and monitoring regressions. The role requires close collaboration with model, compiler, runtime, platform, and hardware teams to establish performance targets and translate measurements into actionable recommendations.

Own and extend Wayve’s machine learning compilation pipeline from model checkpoint to deployable embedded bundles across NVIDIA TensorRT and Qualcomm QNN targets. Design compiler passes, quantization and precision-typing systems, graph partitioning, legalization, regression testing, and benchmarking infrastructure. Partner with model and training teams to ensure compilability, accuracy, and latency across architectures and SoCs. Provide technical direction and mentorship for compiler engineering.

Own Soda’s technical content strategy across its website, blogs, webinars, videos, and personal audience. Build automated, agentic content production workflows using LLM pipelines, structured outputs, and evaluations while maintaining high editorial quality and distinct voices. Write for technical data audiences, create content independently, and use Python or TypeScript, APIs, webhooks, and databases to automate growth operations. Collaborate closely with founders, engineers, and customers in a distributed remote environment.

Own Soda’s technical content function across its website, blogs, webinars, videos, and audience growth. Build automated, agentic LLM content pipelines with structured outputs and evaluations while maintaining strong editorial judgment, distinct voices, and high-quality technical writing. Develop independently using Python or TypeScript, APIs, webhooks, and databases. Translate data quality and infrastructure concepts for technical audiences, collaborate with founders and customer engineers, and continuously improve workflows based on content performance.

Lead the architecture and deployment of enterprise agentic AI platforms using LangGraph, LLMs, RAG, enterprise integrations, cloud services, and secure governance patterns. Design scalable multi-agent workflows, state management, observability, evaluation, and human-in-the-loop controls. Partner with senior stakeholders, guide engineering teams, establish reusable architecture standards, and ensure production AI systems meet security, reliability, compliance, and operational requirements.

Designs and operates scalable data pipelines, warehouse models, and analytics solutions. Builds natural-language data interfaces using GenAI and LLM techniques, optimizes SQL, improves data quality and observability, and supports distributed data platforms on AWS or GCP. Partners with engineering, analytics, product, and business teams while contributing to standards, testing, documentation, code reviews, production support, and incident response.

Develop and improve InstaLILY’s core AI platform, collaborating with product, design, UI/UX, and business teams to deliver reliable customer-focused features. The role includes writing software, testing, supporting CI/CD practices, maintaining technical quality, and staying current with AI and software engineering technologies. Candidates should have 0–2 years of software engineering experience, programming experience in Python and JavaScript, and familiarity with cloud computing or AWS, Azure, or GCP.

Lead customer-facing delivery of production-grade data and AI solutions using Databricks. Own architecture and design decisions across data engineering, distributed computing, ML/AI integration, and application development. Embed with enterprise customers, guide technical teams, manage delivery scope and outcomes, train stakeholders, resolve implementation issues, and contribute reusable frameworks and product feedback. The role requires hands-on coding, cloud expertise, Spark experience, technical project delivery, and approximately 20% customer travel.

Provides senior technical leadership for enterprise data architecture and delivery across Google Cloud engagements. Designs scalable data platforms, pipelines, models, integrations, governance, security, and observability solutions; supports hands-on implementation; develops technical estimates and proposals; partners with cloud, data, and AI teams; establishes architectural standards; mentors engineers; and contributes to client solutioning and pre-sales activities.

Provides valuation, P&L, risk measurement, monitoring, and reporting for energy trading activities. Explains P&L and risk drivers, monitors exposures and limits, supports product onboarding and valuation verification, and advises stakeholders. The role also improves Product Control processes through automation and tools using Excel, VBA, SQL, Python, and Power BI, while contributing to risk initiatives and business projects.

Research and develop methods for detecting misalignment and loss-of-control risks in frontier AI systems. Build alignment evaluations, conduct pre-deployment testing, analyze model behavior, investigate incidents, and communicate findings to AI companies and governments. The role also involves designing evaluation software and tooling, publishing technical research, threat modeling, and mentoring collaborators. Candidates need substantial AI safety or alignment research experience, strong Python and machine learning engineering skills, and experience with frameworks such as PyTorch or Inspect.

Design, build, optimize, and productionize machine learning pipelines, data engineering workflows, tooling, and low-latency Python/C++ systems. Partner with quantitative researchers to turn trading model prototypes into scalable production systems. Responsibilities include architecture design, performance optimization, automated testing, cloud deployment, model serving, and supporting real-time inference for sports betting analytics.

Build and deploy LLM-powered workflows for bioinformatics and metabolomic chemistry. Translate scientific questions into reliable AI tasks, query biological data and knowledge graphs, generate traceable recommendations, and develop benchmarking and evaluation infrastructure. Collaborate with wet-lab scientists, chemists, microbiologists, and engineers while contributing Python code, data architecture, pipelines, and model deployment to a shared production codebase.

Develop agentic LLM workflows and contribute Python code to support decision-making in bioinformatics and metabolomic chemistry. The intern will design workflow architectures, prompts, tool integrations, benchmarks, tests, and evaluation processes using proprietary biological data. This applied research role involves collaboration with bioinformaticians, data scientists, wet-lab scientists, automation scientists, and software engineers, with opportunities to deploy production code used by scientists. The placement is fully immersive and on-site in London for at least three months.