Top Python Jobs in London

Build end-to-end software solutions and integrations for strategic customers. Partner closely with customer engineers and executives, design technical architectures and product features, apply ElevenLabs technologies, and solve complex problems through hands-on coding and implementation. The role requires strong Python and software development expertise, API integration knowledge, clear communication, and regular travel to customer sites.

Forward Deployed Engineers build and deploy custom software solutions for Swedish government and commercial customers in Stockholm. Responsibilities include integrating platforms with data sources and IT systems, developing applications and data pipelines, rapidly prototyping, collaborating with analysts and operators, leading field technical delivery, and informing the product roadmap. The role requires full-stack or systems engineering expertise, strong communication, and adaptability in high-stakes, ambiguous mission environments.

Design, build, operate, and maintain scalable distributed systems supporting CrowdStrike’s Intelligence pipelines. Gather requirements from analysts, develop processing and data integration systems, make autonomous architecture decisions, maintain Go and Python codebases, deploy production services, document solutions, train users, and communicate technical details effectively. The role requires independent execution in a geographically distributed team and includes opportunities to work with cloud platforms, APIs, web interfaces, malware analysis, and AI technologies.

Build and operate AI backend systems, agentic workflows, RAG pipelines, evaluations, and LLM-powered data pipelines. Develop data-layer capabilities, monitoring, inspection interfaces, and production-grade software across Python, Dagster, PostgreSQL, Elasticsearch, and Iceberg. Collaborate with product, research, and commercial teams, own technical projects and PoCs, and balance security, quality, and delivery speed in a high-ownership startup environment.

Provide premium customers with hands-on technical troubleshooting and end-to-end ownership for complex API, integration, authentication, performance, and production issues. Analyze logs and system behavior, coordinate incident response, communicate with technical and executive stakeholders, monitor customer integrations, develop runbooks, lead postmortems, and translate recurring problems into product, tooling, monitoring, and automation improvements.

Lead the design, build, and productionisation of Fundment's AI systems and infrastructure on GCP. Define AI strategy, architect LLM/RAG/agent solutions, implement MLOps, model governance, IaC, monitoring, cost optimisation, and mentor a multidisciplinary team to embed AI into products and operations.

Clean, transform, document, and upload customer business data into Watershed templates. Advise customers on data solutions, apply data-cleaning best practices, ensure traceability for verification, and synthesize feedback to improve services and software. The role requires strong Excel skills, analytical judgment, attention to detail, customer empathy, and clear communication.

Build production machine-learning features that transform raw AI usage data into customer-facing insights. Own classification, clustering, data pipelines, model deployment, evaluation frameworks, ground-truth datasets, error analysis, uncertainty quantification, and production reliability. Collaborate with customers, product, engineering, and security teams to shape insight experiences. Work primarily in Python across a polyglot Java, Spring Boot, and TypeScript stack deployed on AWS.

Works with account sales teams, partners, and customers to demonstrate Snowflake solutions, lead proofs of concept, develop technical champions, and drive technical wins. Presents to executives and technical audiences, translates business needs into data and cloud architectures, supports evaluations and implementations, tracks competitive technologies, and collaborates with Product, Engineering, Marketing, and implementation partners.

Owns technical outcomes for strategic customer engagements, from discovery and architecture through implementation, prototyping, productionization, adoption, and handoff. Builds AI-powered solutions using customer data, evaluates business value, develops reusable accelerators, and integrates enterprise systems. Partners with sales, product, R&D, and implementation teams to validate customer needs, influence deals, and turn recurring requirements into roadmap investments. The role requires customer-facing communication, software and AI engineering, enterprise integration expertise, and travel to customer sites.

Senior Solutions Architect in MongoDB’s pre-sales organization, guiding enterprise customers in designing scalable, reliable applications and distributed systems using MongoDB and cloud technologies. Responsibilities include architecture reviews, technical discovery, demonstrations, proofs of value, sizing, documentation, account planning, demand generation, and customer success. The role partners closely with sales teams, advises technical and business stakeholders, mentors colleagues, and travels up to 25%.

Own post-booking product areas for US credit cards, including payments, collections, credit limit increases, spending, and the mobile app. Run customer experiments, conduct research, collaborate with credit risk and data science, prototype solutions with AI, build automations, and launch improvements. The role requires comfort with ambiguity, data-driven decision-making, rapid testing, and hands-on execution across product, engineering, operations, legal, and compliance.

Own post-booking credit card product areas including payments, collections, credit limit increases, spending, and the mobile app. Run customer experiments, conduct research, use data and AI to test opportunities, collaborate with credit risk and data science, build automations, and deliver solutions with engineering, operations, legal, and compliance teams.

Maintain, troubleshoot, optimize, and scale physical and virtual server and network infrastructure. Configure Cisco switches, diagnose connectivity and performance issues, manage hardware and Linux systems, monitor infrastructure, and support mission-critical environments. Develop Python automation for network administration and infrastructure deployment while collaborating with senior engineers. Provide occasional out-of-hours support for incidents, deployments, and maintenance windows.

Leads the design, development, evaluation, and deployment of scalable machine learning solutions for complex business challenges. Identifies high-value AI opportunities, researches emerging methods, builds evaluation frameworks, and improves model performance and reliability. Partners with engineering, data, product, and business teams to deliver production-ready systems, provides technical leadership, mentors scientists, and communicates recommendations to technical and non-technical stakeholders.

Design and implement secure cloud infrastructure using infrastructure as code, automation, CI/CD pipelines, monitoring, and security controls. Build scalable AWS solutions with Terraform, CloudFormation, and AWS CDK; automate provisioning, configuration, compliance, cost management, and operational tasks. Integrate application and infrastructure security testing, container scanning, observability, and secret management while troubleshooting cloud issues programmatically.

Design, architect, and implement production-grade agentic AI systems. Drive agentic product direction, identify opportunities, build prototypes into scalable production code, integrate with partners and platforms, and establish evaluation, observability, and regression-testing practices. Collaborate closely with Product and support customer and partner technical integrations while raising engineering standards across the applied-AI team.

Lead product analytics and experimentation across digital products and e-commerce. Analyze customer behavior using funnels, journeys, cohorts, segmentation, and retention; define and govern product metrics; validate tracking and data quality; guide experimentation; and translate findings into product recommendations. Partner with Product, Data, and Engineering teams to improve evidence-based decision-making and build repeatable analytical frameworks, documentation, and team capabilities.

Build and operate a scalable evaluation framework for computer-use agents and AI models. Responsibilities include benchmark integration, orchestration, scheduling, observability, reproducible trials, distributed systems operation, API and external-service integrations, customer collaboration, and framework standards. The role requires production backend development in Python, Kubernetes and public-cloud experience, database and messaging knowledge, and end-to-end system ownership.

Build and own production data pipelines for YouTube, AVOD/FAST, Google Ad Manager, and Google Ads sources on GCP and BigQuery. Develop conformed dimensional data models, campaign-to-brand attribution, data-quality checks, reconciliation logic, monitoring, and incident response. Optimize BigQuery performance and cost while applying engineering standards, testing, CI/CD, documentation, and lineage. Partner with finance, network, and commercial stakeholders to create reliable reporting models.

Supports corporate data initiatives across financial, client, employee, and operational datasets. Responsibilities include data integration, modelling, quality remediation, governance, master data management, stakeholder engagement, and delivery of data products across strategic transformation projects. The role monitors data quality, develops controls and standards, supports multiple concurrent initiatives, and advises business and technology teams on data management best practices.

Summer 12-week software engineering internship at Databricks for students graduating between fall 2027 and summer 2028. Interns will collaborate with engineers to design, implement, test, and deliver platform features across areas such as backend, infrastructure, cloud, databases, and customer-facing products. Candidates need programming experience in Python, Java, or C++, along with knowledge of algorithms, data structures, and object-oriented programming.

Lead the analytics team, manage analyst performance and development, prioritize business data requests, and deliver reporting, dashboards, analysis, and operational controls. Partner with Product and cross-functional teams on feature tracking and reviews, maintain data and coding standards, oversee automation and tooling, and ensure data quality. The role requires strong SQL, BI, stakeholder management, data storytelling, and the ability to translate business needs into technical requirements within a regulated fintech environment.

Build and optimize scalable Databricks data pipelines using Delta Lake, Unity Catalog, Spark, PySpark, Python, and SQL. Collaborate in an Agile team with product and marketing stakeholders to deliver data-driven solutions. Extend the Azure data platform through Azure Data Factory and Function Apps, implement CI/CD practices, improve platform performance, and contribute to AI-assisted development and continuous modernization.

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.