Top Python Jobs in London

Build and maintain Anaplan models supporting FP&A, capital, liquidity, and group viability forecasts. Manage Anaplan-Snowflake data pipelines, own data inputs and outputs, streamline forecasting workflows, optimize model performance, and translate complex financial requirements into scalable technical solutions. Support stress testing and going-concern assessments while collaborating with finance, accounting, reporting, and technology stakeholders.

Build and own Ohme’s scalable analytics platform, including Snowflake, dbt data models, semantic layers, data quality, observability, security, and cost optimization. Develop reliable, documented analytics assets, establish engineering standards, support analysts and business users, and use AI tooling to improve team efficiency. The role requires strong SQL, dimensional modeling, stakeholder communication, and ownership of warehouse reliability and performance.

Owns the architecture, development, and scaling of data products, backend data services, ingestion engines, APIs, and infrastructure on AWS and Snowflake. Collaborates with engineering and data teams, automates infrastructure using AWS CDK, optimizes reliability and latency, and ensures data quality through testing and validation. The role applies a product mindset to customer-facing and internal data services within an EV charging and smart energy platform.

Design, build, deploy, and optimize production AI systems, including LLM integrations, RAG pipelines, embeddings, vector search, predictive models, and inference systems. Collaborate with product and engineering teams to translate business needs into scalable solutions for recommendations, risk scoring, forecasting, and workflow automation. Own data pipelines, monitoring, evaluation, security, privacy, documentation, and continuous model improvement across cloud environments.

Build and maintain full-stack web applications using React and backend technologies, develop ETL jobs for customer data in the Yext Knowledge Graph, improve development tooling, advise stakeholders on technical tradeoffs, collaborate with product, design, and project management, and deliver clean, tested code through Scrum-based development.

Build and improve production AI systems including chatbots, document extraction pipelines, LLM workflows, and voice AI. Responsibilities span discovery, prototyping, Python development, deployment, evaluation, monitoring, and process automation. The role requires applying LLMs, retrieval systems, prompts, and agent workflows to simplify financial-services operations and autonomously complete customer account actions.

Build and improve production AI systems for fintech operations, including chatbots, document extraction pipelines, LLM workflows, and autonomous account actions. Responsibilities span opportunity discovery, rapid prototyping, prompt and retrieval optimization, deployment, evaluation, monitoring, and process automation. The role requires strong Python engineering, practical LLM experience, sound judgment in ambiguous situations, and the ability to communicate trade-offs and influence stakeholders.

Analyze scientific publishing and content acquisition data to guide product and business decisions. Design metrics, maintain analytics products and dashboards, conduct quantitative research, uncover trends and opportunities, and promote data literacy. The role requires advanced SQL and Python, data visualization, cloud data warehouse, and analytics engineering experience, including ETL pipelines, data modeling, and testing.

Assess and document an existing Azure ML environment, including data scientist workflows, training and deployment pipelines, model assets, dependencies, integrations, and data flows. Support migration and testing of pilot machine learning models, recommend optimization opportunities, and apply Azure DevOps and CI/CD practices to production model lifecycle management.

Lead attack surface and vulnerability management activities, including CNAPP assessments, cloud configuration reviews, threat intelligence analysis, vulnerability prioritization, and urgent cyber defense response. Partner with developers and application custodians to improve CI/CD security, automate workflows with scripting and AI, establish CIS-aligned security baselines, assess third-party exposure, and develop security documentation, policies, and standards.

Designs and delivers cloud-based data solutions for clients, advises on data landscapes, governance, quality, and maturity, and shapes data strategies and roadmaps. Leads requirements workshops, develops scalable solutions using Azure, Fabric, Databricks, SQL, and Python, implements security practices, manages multiple delivery workstreams, supports junior staff, and contributes to proposals and business development.

Lead the architecture and delivery of enterprise analytics and AI solutions across Microsoft Fabric, Databricks, Power Platform, Copilot, and Power BI. Design AI-ready data architectures, semantic models, metric layers, and ontologies; evaluate emerging technologies through proofs of concept; establish governance standards and reusable patterns; optimize platform roadmaps, performance, monitoring, and costs; and mentor teams while modernizing legacy analytics platforms.

Analyzes forecasting, inventory, sales, and stock-flow data to improve demand-weighted availability, inventory turns, stock placement, and supply chain performance across retail and e-commerce. Investigates exceptions, conducts root-cause analysis, coordinates backstage stock control, supports store openings, and collaborates with forecasting, inventory, retail operations, and space teams on continuous improvement initiatives.

Develop scalable machine learning classifiers, predictive models, deep learning systems, and Generative AI applications for production. Build reusable AI assets, libraries, and services; collaborate with software and data engineers; apply prompt engineering, retrieval strategies, and ML workflows; and advise customers on AI adoption. The role also requires expertise in statistics, model fairness, interpretability, cloud environments, and production Python development.

Provides technical onboarding and implementation services for enterprise customers from proof of concept through production. Manages multiple customer accounts, translates technical needs into Sectigo solutions, troubleshoots implementation issues, and coordinates internal and external stakeholders. Maintains customer communications and ticket updates while contributing to onboarding process improvements, documentation, knowledge-base development, and readiness programs. The role is hybrid in London and requires expertise across PKI, certificate authorities, Windows, Linux, cloud, networking, and scripting.

Conducts quantitative genomics and statistical genetics analyses integrating human genetics, sequencing, EHR/claims, real-world evidence, and AI/ML data. The role defines patient populations, biomarkers, disease segments, and diagnostic pathways; manages external collaborations; evaluates data providers and cohorts; supports clinical and commercial patient-finding strategies; informs portfolio prioritization; and communicates evidence-based recommendations across scientific and business stakeholders.

Lead the architecture, development, and deployment of secure, scalable machine learning systems and AI safety evaluation tools. Design production-grade ML infrastructure, establish deployment standards, advise clients, collaborate with technical and commercial teams, and mentor engineers. The role focuses on operationalizing LLM applications, supporting frontier AI model testing, and delivering robust systems for high-stakes national security and AI safety missions.

Build and deploy production-grade machine learning software, tools, and infrastructure for high-stakes AI safety and national security applications. The role involves creating scalable ML solutions, evaluating frontier AI models, making architectural decisions, advising clients, and collaborating with engineers, data scientists, commercial teams, and research labs. Candidates should have strong Python and software engineering skills, cloud and containerization experience, ML lifecycle knowledge, and familiarity with LLM applications and AI safety evaluation.

Lead the architecture, development, deployment, and scaling of secure, production-grade machine learning systems and AI safety evaluations. The role includes technical scoping, cloud infrastructure design, client advising, cross-functional collaboration, engineering standards, and mentoring. Candidates need strong Python and software engineering expertise, experience with LLM applications, TensorFlow or PyTorch, cloud platforms, cybersecurity, Docker, Kubernetes, and full ML lifecycle operations.

Build and scale AI-powered systems in collaboration with data scientists and product managers. Develop reliable frontend and backend architectures, data pipelines, and cloud infrastructure. Deploy machine learning outputs into production-ready applications, provide technical expertise to customers, and manage the full software lifecycle while balancing rapid prototyping with long-term maintainability.

Build and maintain Kafka Connect services and streaming data pipelines for Depop’s data platform. Extend the bronze streaming layer, implement data contracts and freshness checks, develop quarantine and alerting systems, support event onboarding and troubleshooting, document technical decisions, and collaborate with senior engineers and teams across data infrastructure, ML, and insights.

Owns Parameta Solutions’ global Benchmarks and Indices business, including P&L, strategy, product development, commercialization, governance, and client relationships. Leads research, technical, operations, sales, and cross-functional teams while developing investible multi-asset index products, modernizing calculation platforms, ensuring regulatory compliance, and driving revenue growth.

Conduct research on robot learning for humanoid robots, focusing on reinforcement learning, world models, multimodal VLA models, pretraining, inference optimization, and real-time edge deployment. Interns will train and evaluate policies in simulation and real-world environments, develop generative models, optimize distributed GPU workflows, and complete a personally collected-data robotics challenge using a public GitHub repository.

Support the deployment, operation, automation, and troubleshooting of Microsoft Azure and AWS cloud environments. Maintain CI/CD pipelines, use Git and Infrastructure as Code, assist with monitoring and security improvements, and contribute to technical documentation and agile delivery. The role is designed for an early-career engineer with practical cloud or DevOps exposure who wants to develop broader multi-cloud, automation, and platform engineering capabilities.

Owns the operational lifecycle of LLM, GenAI, and RAG systems, including deployment, monitoring, evaluation, observability, versioning, cost optimization, and production reliability. The role builds ML platform capabilities for regulated clinical workflows, ships agentic or RAG pipelines, establishes human-feedback evaluation loops, and partners closely with the existing MLOps team.