Top Python Jobs in London

As a Software Engineer at Landytech, you will develop and maintain APIs for financial metrics, optimize systems, and collaborate with the team on scalable data solutions.

Partner with quantitative traders and researchers to design, build, deploy, and support production-grade Python reporting and analytical tools, integrate alternative datasets, and deliver insights for trading strategies and performance monitoring.

The Technical Lead will oversee the design, development, and implementation of AI-driven projects, collaborating across teams and managing technical strategy while being client-facing.

Senior actuarial analyst to develop, program, and support Everest's global reinsurance pricing tool. Collaborate on UI, architecture, testing, and user support; troubleshoot production issues; prepare documentation and train users. Combine actuarial reinsurance expertise with software development and data manipulation skills to support pricing during renewals and wider group actuarial needs.

As a Machine Learning Engineer, you'll train and deploy ML models for enterprise clients, work on deep learning solutions, and interface with product teams to enhance AI applications.

As a Solutions Engineer, you'll engage with customers to design AI solutions, deliver demos, and collaborate with teams, ensuring technical needs are met.

As an AI Engineer at Wavestone, you'll lead AI-driven transformation projects, develop production-grade AI solutions, and mentor team members while shaping innovative AI strategies.

Join AllianceBernstein's Systematic Fixed Income team to develop, backtest, and implement systematic fixed-income and credit strategies. Responsibilities include portfolio optimization, factor discovery and analysis, risk attribution, and contributing to a Python-based research platform (abAlphaLabs). Collaborate across the investment process and support ongoing management and enhancement of systematic strategies.

The Senior Client Platform Engineer will manage and automate macOS, iOS, Android, ChromeOS, and Windows endpoints, improving scalability and efficiency.

Design, build, test, and operate Astronomer’s production platform at scale. Own CI/CD, deployment, reliability, and observability practices; collaborate cross-functionally to set platform standards, participate in incident management, and document systems and processes.

Execute and monitor day-to-day capital deployment across DeFi protocols, manage institutional wallet and custody workflows, reconcile deployed capital and yields, monitor liquidation and protocol risk, and maintain quantitative models for yield optimization and protocol integrations. Interface with accounting and finance and operate within firm-approved guardrails to preserve capital.

Develop, test, and deliver conversational chatbot solutions using Cognigy and NICE Proactive AI Agent. Implement and enhance features, write and review JavaScript code, unit test, resolve defects, support production, perform system analysis, and present client-facing demos. Mentor team members and ensure timely delivery.

Build and operate DV's model layer: fine-tune and distill open-weight models, run on-prem inference infrastructure on Kubernetes, implement a model gateway routing across providers, evaluate model quality/cost/latency, manage GPU provisioning and utilization, and define tooling for model selection and production regression testing.

Design, build and optimize scalable AWS-based data pipelines and ETL processes (batch and near-real-time) using Python, SQL, Airflow, Glue and Redshift. Improve performance, reliability and cost efficiency, implement monitoring/alerting, support governance, security and compliance, collaborate with architecture and analytics teams, and mentor colleagues.

Partner with account teams to design and present customised portfolio analyses and liability-aware investment solutions (LDI/CDI) for institutional clients, especially pension schemes. Produce thought leadership, leverage quantitative modelling and AI-enabled tools, and act as a trusted advisor to support business development and client servicing across fixed income, multi-asset and private markets.

Design, build, and deploy production-grade agentic AI systems: multi-agent orchestration, RAG pipelines, LLM provider integration, LLMOps (eval harnesses, prompt versioning, observability), cloud-native deployment, and reusable patterns while embedding with client engineering teams to measure and improve agent accuracy, latency, safety, and cost.

Design, build, and deploy production-grade multi-agent AI systems for enterprise clients. Own RAG pipelines, multi-LLM integration, LLMOps (eval, prompt governance, observability), orchestration frameworks, and lead client engineering engagements and engineering teams.

Build and maintain Dialpad's native Salesforce integration and other integrations, design architecture, write clean modular code with unit tests, participate in code reviews, mentor engineers, support ETL pipelines, and contribute to Agentic AI initiatives to enable real-time automations and data flows.

Join the Core backend team to develop resilient platform infrastructure powering B2C, B2B and API features. Build and refine critical flows for OCPI charging sessions, evolve tariffs/pricing logic, improve observability and scalability, and collaborate cross-functionally to deliver robust technical solutions.

Lead design and implementation of high-performance agent runtimes, backend services, and self-hosted model/agent infrastructure. Build low-latency execution systems, real-time data pipelines, telemetry loops, CI/CD for zero-downtime deployments, and ensure fleet reliability on AWS/EKS.

The Senior Software Cybersecurity Engineer analyzes software for security, performs threat modeling, leads security efforts, mentors teams, and integrates security throughout the development lifecycle.

Lead budgeting, forecasting, and variance analysis while building AI-driven financial models, scenario planning, KPIs, and dashboards. Automate FP&A processes, deliver insights to senior leaders, and collaborate with cross-functional teams to improve forecast accuracy and strategic decision-making.

Design, implement, and optimize AI-driven solutions across the claims lifecycle (intake, investigation, adjudication). Build ML models for data extraction, fraud detection, reserve setting, subrogation, and adjudication decision engines. Collaborate with underwriting, fraud, legal, and IT to integrate models into operations and improve efficiency, accuracy, and compliance.

Lead modernization of month-end and year-end close processes using AI and automation. Prepare, review, and validate journal entries, accruals, and financial statements. Implement anomaly detection, automated reconciliations, variance analysis, and predictive insights. Collaborate with data teams to structure finance data for ML use cases and design audit-ready, scalable reporting workflows.

Design, implement, and monitor AI/ML models to improve underwriting risk assessment, pricing, and portfolio management. Translate underwriting rules into data models, integrate models into workflows, analyze datasets for risk patterns, build decision-support tools, ensure compliance and fairness, and collaborate with underwriting, actuarial, product, and engineering teams.