Top Python Jobs in London

Join a small founding engineering team to design, build, and maintain scalable backend services and APIs for an FCA-regulated trading platform. Take end-to-end ownership of features, contribute to system design and architecture, maintain high code quality and testing standards, monitor and improve reliability, and participate in on-call production support. Work across the stack to ship secure, performant software and help shape the engineering culture as the company scales.

Work on infrastructure reliability and automation: manage incidents, assist dev teams, build pipelines, migrate services, maintain resilience, and create dynamic environments.

Design, build, and maintain high-volume distributed data processing jobs and pipelines (Redshift, Athena, Iceberg, Spark, Airflow). Collaborate with data scientists and analysts to productionize models, optimize performance, troubleshoot systems, and mentor junior engineers. Support lakehouse/data warehouse management and ensure data accuracy and efficiency.

Lead integration of a counter-UAS initiative combining robotics, ML, and systems engineering. Build prototypes, run trials, and implement sensor fusion, vision models, state estimation, and hardware/software integration alongside product and external partners.

Join the global IDI team to implement and maintain systematic trading implementations, automate processes, perform deep-dive data analysis, build tooling and emerging AI capabilities, and support portfolio management and trading operations. Complete 18 months of structured rotations before returning to IDI to lead cross-functional implementations and present findings to senior stakeholders.

Senior network engineer responsible for designing, implementing and supporting enterprise connectivity across LAN, WAN, data centre, cloud and container platforms. Lead hands‑on engineering, cloud‑native/OpenShift networking, low‑latency trading connectivity, timing systems, monitoring, automation (Ansible/Terraform/Python) and vendor platforms (Cisco, Arista, F5/A10, Palo Alto, Fortinet). Ensure compliance, incident response, operational readiness, and mentor colleagues while contributing to architecture and connectivity roadmap.

Lead and develop an Infrastructure Reliability Engineering team to implement SRE practices across infrastructure platforms. Drive observability, resilience validation, automation (IaC), operational readiness and SLIs/SLOs, reduce operational toil, and improve service reliability. Collaborate with operations, security and delivery teams to identify risks, run resilience testing, govern observability standards, and deliver engineering improvements for secure, recoverable, and supportable infrastructure services.

Lead analytics projects by transforming large datasets, applying intermediate statistics, building visualizations and narratives, partnering with stakeholders to define KPIs, mentoring junior analysts, and leveraging modern analytics and AI tools to deliver data products and business insights.

Originate and manage eFX client relationships through proactive lead generation, targeted outbound sales, product demonstrations, pipeline management, and collaboration with electronic trading, quant research, client service and onboarding to drive revenue and market share while ensuring regulatory compliance.

Lead design and delivery of production-ready AI integrations to improve developer productivity. Partner with engineering teams to translate AI opportunities into architecture, reference implementations, and handoff-ready proofs-of-concept while ensuring reliability, monitoring, security, and adoption.

Lead incrementality measurement and causal inference for performance marketing. Design and run experiments, build reproducible measurement frameworks (MMMs, PSA, GeoLift, holdouts), translate results into commercial impact, and partner with marketing, product, and buying teams to operationalize findings for planning and optimization.

Design, develop, deploy, and improve production-grade machine learning and computer vision models. Evaluate model performance, analyze data, support MLOps/CI-CD pipelines, resolve production issues, and mentor junior engineers while collaborating with product and engineering teams to deliver robust, scalable ML solutions.

Lead technical deal cycles for enterprise and commercial accounts: perform technical discovery, design solutions, build POCs and AI-driven workflows, configure integrations and APIs, ensure security/compliance, and advocate product feedback to internal teams.

Contract Data Centre Network Engineer to design, deploy, and operate high-performance data centre networks (leaf-spine, EVPN/VXLAN), manage routing (BGP/OSPF), automate configurations (Python/Ansible/Terraform), troubleshoot network and Linux systems, support Kubernetes networking, and contribute to monitoring, documentation, and capacity planning for AI/GPU infrastructure.

Lead technical strategy and delivery for Revenue Intelligence systems across web apps, backend APIs, event-driven services, data pipelines, analytical stores, and ML recommendation workflows. Improve reliability, security, scalability, and observability while remaining hands-on in design, implementation, incident response, and mentoring to raise organizational engineering effectiveness.

Design, build, and maintain scalable batch and streaming data pipelines, internal tools, and APIs to support analytics, product features, and ML. Collaborate with product and engineering to improve data infrastructure, ensure reliability, testing, and observability in a cloud-native environment.

Build and scale AI-driven automation solutions using LLMs and agent frameworks. Design multi-step workflows, integrate AI with internal systems (HR, finance, legal, ticketing), automate document processing and data extraction, and train internal teams. Collaborate with technical leads, product, and IT to deliver practical, production-ready automation and iterate quickly.

Lead threat detection and response efforts: research adversary behavior, monitor environments, respond rapidly to incidents, develop SIEM/SOAR use cases and playbooks, perform forensics, automate detection/remediation, produce reports, and mentor junior analysts.

Design and deploy dynamic, feedback-driven pricing systems for a bid-based marketplace. Model elasticity, supply/demand responses, and long-term feedback; develop real-time pricing algorithms; run and analyze A/B experiments; productionize scalable models with engineering; monitor system stability and contribute to methodological choices including optimization and RL-inspired approaches.

Design, develop, and deploy enterprise-grade Azure AI solutions including LLM-powered chatbots, vector-search integrations, and autonomous agents. Collaborate with cross-functional teams to build secure, governed, scalable AI platforms, optimise data flows, and support AI adoption and engineering best practices.

Lead Site Reliability Engineering for Azure cloud platforms: define SLOs/SLIs, improve observability, drive automation, manage incidents and root-cause analysis, collaborate with engineering and product teams to ensure scalable, reliable deployments and operational excellence.

The role involves client-facing responsibilities in technical configuration, data extraction, engineering, science, and deployment of analytics solutions, requiring effective communication and project management skills.

Design, build, and maintain scalable data pipelines and storage for fraud detection. Support fraud analytics, feature engineering, model integration and serving, pipeline monitoring, and documentation. Collaborate with data, product, and engineering teams to enable detection, reporting, and operationalisation of fraud models.

Lead investigation and resolution of broker margin data discrepancies by validating Python pipeline outputs against internal sources. Debug raw broker files, document margin calculation logic across account types, work with Data Engineering to translate business rules into Python, escalate structural data gaps, liaise with brokers and SMEs, and maintain mapping and audit documentation.

The Machine Learning Engineer will research algorithms for networking, develop ML infrastructure, maintain documentation, and assist in technical communication.