Top Python Jobs in London

Lead Full Stack Engineer to own solution design and lead a small engineering squad. Responsibilities include end-to-end feature design, delivery leadership (planning, prioritising, ceremonies), hands-on development of complex backend (Python) and frontend (React/Flutter) features, CI/CD and testing, incident response, and evolving platform architecture, observability, security and developer experience while working closely with founders and partners.

Contract AI Engineer to build an enterprise AI capability: design agentic workflows and Copilot extensions across Microsoft 365 and Salesforce, build production-ready agent solutions (connectors, monitoring, cost controls), translate sales methodology into real-time coaching and CRM insights, and collaborate with GTM leadership to drive behaviour change. Hybrid role in London for 4-6 months (3-4 days/week).

Serve as the bridge between research, product, and engineering to translate financial models into production; integrate advanced financial modeling into the platform; lead development of an automated data extraction and analysis tool; own product development lifecycle; collaborate across teams and produce clear, actionable analytical reports for stakeholders.

Own and operate end-to-end ML infrastructure: build and maintain Airflow pipelines, CI/CD and IaC for model training/deployment, manage MLflow registry, deploy to AWS and edge devices, implement monitoring/data-versioning, ensure HIPAA/SOC2 compliance, and support reproducible, reliable ML in production.

Research, prototype, and deploy ML models for physiological sensing (radar, audio, other signals). Design experiments, implement production-grade models for cloud and on-device inference, optimize for constrained compute, collaborate with clinical and engineering teams, and contribute documentation and regulatory evidence.

The Staff ML Performance Engineer optimizes ML inference for edge devices, profiles performance bottlenecks, and implements improvements across systems while collaborating with model developers to enhance on-device performance.

Design and implement AI-powered internal tools for GTM use cases, managing full lifecycle from data modeling to production. Collaborate closely with GTM teams to translate data into actionable insights with a focus on measurable business impact.

As a Marketing Analytics & Business Intelligence Analyst, you will analyze data for marketing insights, inform product strategies, and drive business growth through analytics and reporting.

The role involves building and scaling full-stack applications for document analysis, collaborating with AI engineers, and establishing engineering best practices.

You will design, build and refine AI systems for legal applications, focusing on LLM agents and data evaluation frameworks.

Lead design and delivery of a Snowflake-based cloud reporting and data analytics platform for SwapClear & Listed Rates. Own migration from Oracle/Informatica, ensure availability, performance, governance and lineage, drive technical standards, mentor engineers, partner with architecture, governance and business stakeholders, and support quality, observability and multi-year roadmap delivery.

Develop systems that enhance high-performance trading platforms, focusing on workload distribution and real-time support for quantitative researchers.

Work within the Data Analytics team to drive data-driven decision making: lead analytics projects, model and simulate business problems, produce end-to-end reporting, visualise insights for technical and non-technical stakeholders, provide technical oversight and mentor colleagues.

As a Full-Stack Engineer, you'll collaborate with the engineering team to design and launch products across the entire stack, from front-end to back-end and infrastructure.

Work directly with customers to onboard datasets, design synthetic population queries, diagnose and improve data pipelines, validate synthetic methodologies, lead technical engagements, train and document for adoption, and feed customer insights into product priorities.

Design and build ML, NLP, and generative AI systems for scientific discovery and knowledge extraction. Work with large heterogeneous scientific datasets to develop semantic search, entity extraction, QA, summarization, embeddings, and production-ready models. Implement model evaluation, fine-tuning, deployment, monitoring, drift detection, and scalable data pipelines while collaborating across engineering, product, UX, and research teams.

Design, build, and deploy machine learning models to detect fraud, prevent losses, and monitor risk. Work with Product, Engineering, and Operations to turn large-scale data into production-ready pipelines and model-serving solutions, monitor performance, and continuously improve model accuracy and business impact.

As a Lab Informatics Engineer, you'll implement and optimize lab informatics systems, partner with scientists to create digital solutions, and enhance data interoperability.

As a QA Engineer, you will design and automate tests to ensure the reliability and performance of Fogsphere's AI platform, collaborating with engineering teams throughout the development lifecycle.

The Computer Vision Engineer will design, train, and optimize deep learning models for computer vision, manage data pipelines, and deploy solutions in production environments.

Lead research, development, deployment and real-time management of systematic high- or mid-frequency crypto trading strategies. Own a dedicated P&L-driven risk book, manage position sizing, exposures, drawdowns and capital allocation, optimise models and execution, analyse large-scale time-series data, and communicate performance and risk to stakeholders while collaborating with execution and infrastructure teams.

The role involves converting Python research models into C++ for high-performance trading systems, backtesting, and collaborating with research teams to enhance trading strategies.

Role involves monetising alpha through execution strategies in futures markets, developing execution logic, collaborating on research, and monitoring strategies' performance.

Lead design and maintenance of learner-centric investment education content across CFA Institute products. Translate real-world investment practice into study materials, collaborate with SMEs and cross-functional teams, manage external authors and review cycles, and ensure curriculum alignment, accuracy, accessibility and timely delivery.

Develop scalable cloud infrastructure and robust software solutions for AI/ML, collaborating with engineers and scientists to enhance scientific research.