Top Python Jobs in London

The High-Touch Execution Strategist will manage execution processes, analyze market microstructures, and collaborate with traders to enhance trading practices.

Lead the development of AI-driven solutions, architect scalable AI products, mentor junior engineers, and integrate AI across the organization.

Rotate through BI roles over two years to analyze data, build dashboards, support reporting and forecasting, present insights to leadership, and drive operational improvements.

Join the Data Science team to innovate solutions for data problems, develop forecasting models, and enhance quant strategies using machine learning.

Lead the Redline Product Management team, overseeing Technical Account Management, Sales Engineering, and Product Management while ensuring customer success and driving product initiatives.

Build and maintain large-scale data pipelines and distributed systems to ingest, process, store, and serve petabytes of vehicle data for ML and AD/ADAS development. Collaborate with ML and CV engineers, design cloud-native solutions, and ensure reliability, observability, and automation across data platforms.

Lead design, implementation, and operation of enterprise-scale international data platforms. Build and mentor global data engineering teams, define architecture and governance, ensure scalability, reliability, security, and cost optimization, and partner with analytics and business stakeholders to deliver data solutions and demonstrate ROI.

Maintain and scale DevTools SRE systems: improve user-facing developer workflows, build fault-tolerant self-healing architecture, optimize performance, modify open- and closed-source tools (GitLab, TeamCity), and support users while measuring improvements via metrics.

Intern will patch and upgrade RHEL systems and software, update or build Podman container images, adapt Python/Perl/PHP/shell scripts, migrate databases and jobs to new VMs, test workflows and integrations, and assist in rebuilding a web data-charting tool (Bokeh) and implementing MariaDB replication under senior guidance.

Lead technical strategy and delivery for IRC's program-facing AI portfolio. Manage and grow an engineering and applied-AI team, design and ship agentic systems, predictive analytics, and diagnostics for low-resource settings, oversee Azure-based high-availability architecture, DevOps/CI-CD, security and cost optimization, and partner closely with program leaders and frontier AI organizations to scale responsible, safe AI solutions.

Own and prioritise the product backlog for pricing analytics, translate actuarial pricing needs into user stories, and collaborate with actuarial, underwriting, data engineering and delivery squads. Perform pricing and profitability analyses, validate rating factors and modelling datasets, design data specifications and quality checks, and communicate analytical insights to technical and non-technical stakeholders.

Design, build, and operate a hybrid on‑prem/cloud compute platform for interactive and large-scale batch computing. Develop CI/CD-driven containerized environments, optimize application performance at scale, consult scientists on scalability, ensure observability, testing, documentation, and mentor team members to drive platform adoption and reliability.

Designs, builds, and operates hybrid on-prem/cloud compute platforms, toolchains, CI/CD, and workflows for interactive and large-scale batch scientific computing. Consults scientists on scalability and performance, produces well-engineered software with tests and docs, ensures logging/lineage and platform consistency, and supports transition of HPC users to the platform.

Manage and coach multiple sport-specific data science leads, ensure alignment and delivery of forecasting models, plan resourcing and staffing, maintain technical standards and cross-team collaboration, escalate and resolve technical or resourcing issues, and contribute to department strategy, tooling, and hiring.

Design, train, and deploy retrieval, reranking, and relevance ML models for a production agent-native search platform. Build embedding-based indexing and large-scale retrieval systems, define evaluation metrics and pipelines, optimize latency/quality/cost trade-offs, and collaborate with engineers to integrate models into 24x7 production services.

Lead applied research and system design for agent-native search: develop multi-stage retrieval, ranking, and grounding methods for LLMs working with real-time web data; define evaluation paradigms; optimize relevance, latency, and cost; deploy solutions to production and mentor the engineering team.

Work with customers to design, demo, and deploy generative AI applications using open-source LLMs and Together AI's stack. Run complex POCs, collaborate with sales, provide product and engineering feedback, and create educational content and tooling to enable successful deployment and scaling.

Manage the firm's capital usage, cost allocation, and position management systems; reconcile margin and billing models; execute funding and financing trades; build automation, monitoring, and visualization tools; identify and remediate financing inefficiencies; collaborate across teams and external counterparties.

Build the next evolution of Lawhive's platform working with designers, PMs and AI researchers. Own features end-to-end from design to production, ship TypeScript-based monorepo code, use infrastructure-as-code, run services, respond to incidents, and drive product improvements through customer learning.

Lead design and implementation of ML/LLM systems for ingestion, knowledge extraction, retrieval, and reasoning. Build datasets, metrics, pipelines, agentic retrieval and RAG solutions. Establish MLOps, orchestration, observability, and experiment tracking. Collaborate on research agenda, shape ML architecture, and hire and grow the ML team.

Design, build, and lead development of AppGlass features; develop AI-first tooling and automation; implement enterprise integrations and deployments across Kubernetes and cloud; and improve reliability, testing, performance, and security for production use.

Develop and maintain SFT and RL post-training pipelines for multi-step coding agents, train and adapt LLMs for agent workflows, build evaluation and simulation environments, design metrics and evaluation frameworks, analyze results to improve models and datasets, and collaborate with research, product, and infra teams to ship models into JetBrains IDEs.

Design, train, and deploy large language models from scratch; convert business requirements into technical specs; collect and process pretraining/fine-tuning datasets; run distributed training on large GPU clusters; support and improve production ML subsystems and tooling.

Lead design and implementation of AI-native features for PyCharm and Python tooling. Architect integrations (MCP, agents), prototype and ship features, guide engineering decisions, evaluate AI tools/providers, and expose IntelliJ Platform capabilities to AI workflows while collaborating with product, ML, QA, and senior developers.

Lead analytics for clinical trials by converting complex clinical data into actionable insights, partnering with data quality and monitoring teams, using visualization tools, advancing anomaly detection and risk‑based analytics, and implementing interventions to improve data quality and study timelines.