Top Python Jobs in London

Integrate hardware, firmware, and Windows operating system components into reliable server products. Develop OS images, perform system bring-up, troubleshoot issues across electrical, mechanical, and software boundaries, and create test plans, rigs, fixtures, and automation scripts. Coordinate with engineering teams, suppliers, and contract manufacturers while documenting results, supporting validation, and delivering integration milestones.

Leads digital delivery and optimization across AECOM’s England and Wales water operations. Develops digital strategies, advises clients on transformation, promotes AI, machine learning and data science adoption, identifies automation opportunities, supports MVP development, manages regional delivery enablement, contributes to bids, and facilitates client and stakeholder workshops.

Leads digital delivery and optimization across AECOM’s England and Wales water operations. Develops digital strategies, advises clients on transformation, data governance, operating models, and platform adoption, and promotes AI, machine learning, and data science. Identifies automation opportunities, supports MVP development, manages digital adoption and team capabilities, contributes to bids, and facilitates client workshops and stakeholder presentations.

Leads digital delivery and optimisation across AECOM’s England and Wales water operations. Advises clients on digital transformation, data governance, operating models, platforms and roadmaps; identifies AI, automation and data science opportunities; develops digital strategies and workplans; supports bids; manages adoption and capability development; and facilitates client workshops and stakeholder presentations.

Own the technical sales process for enterprise cybersecurity solutions across Europe, including discovery, solution design, demonstrations, proof-of-value engagements, integrations, training, and executive briefings. Advise security teams on threat intelligence workflows, internet infrastructure abuse, and SIEM, SOAR, TIP, and data-platform integrations. Partner with sales, customer success, product, and engineering teams while providing customer feedback, building reusable technical assets, and supporting renewals and expansions.

Own the enterprise data architecture and technical roadmap for a modern Databricks lakehouse platform. Design medallion architecture, data models, secure integrations, privacy-preserving capabilities, governance, lineage, observability, and AI-ready pipelines. Lead prototypes, technical reviews, optimization, and complex issue resolution while partnering with engineering, product, security, AI, and business stakeholders. Mentor data engineers and architects and establish reusable standards for scalable, reliable, cost-effective data products.

Supports investment stewardship through company engagement, proxy voting analysis, governance and sustainability research, stewardship reporting, and operational process improvement. Partners with investment professionals to assess proxy ballots, monitor regulatory developments, maintain engagement data, develop voting recommendations, and communicate company responses and outcomes. The role also uses data analytics and AI-enabled tools to improve research, workflows, reporting, and engagement tracking.

Lead the design, development, deployment, and support of Microsoft Power Platform and AI solutions. Build secure AI agents and agentic workflows using trusted knowledge, tools, approvals, MCP services, and human hand-offs. Integrate solutions with Azure, Microsoft 365, enterprise systems, and APIs. Establish engineering standards for testing, CI/CD, observability, security, and release management while mentoring developers and supporting live services.

Own multi-cloud platform infrastructure supporting AI workloads, including Terraform, observability, alerting, reliability, CI/CD, disaster recovery, and cost optimization. Improve developer experience and platform performance, support enterprise customers with infrastructure and security questions, contribute to compliance initiatives, and participate in on-call rotations. The role requires strong AWS production experience, hands-on GCP knowledge, deep Terraform and CI/CD expertise, and solid Python skills.

Leads the architecture and delivery of software integrations between the IFS enterprise suite and robotics OEM platforms. Builds abstraction layers for dispatching, orchestrating, auditing, and monitoring heterogeneous robot fleets; personally delivers initial integrations and customer demonstrations. Evaluates OEM technologies, represents IFS with external robotics partners, hires and develops the engineering team, and advises executives and customers on the robotics roadmap. Requires deep hands-on robotics software integration, agentic AI, enterprise APIs, industrial telemetry, simulation, security, and safety expertise.

Own architecture and hands-on delivery of high-priority AI initiatives in the CTO Office. Design, build, evaluate, secure, monitor, and operate production agentic and LLM systems, including RAG and Graph RAG. Work across unfamiliar domains and enterprise platforms, brief executives and customers, and establish a small applied AI engineering team through hiring and coaching. The role requires deep software architecture, production AI experience, rapid execution, and technical leadership.

Analyze pricing, funnel performance, and new product development for the UK Loans team. The role involves framing ambiguous business problems, defining metrics, conducting SQL-based analyses, evaluating pricing strategies, researching improvements, and communicating recommendations to stakeholders. The analyst will collaborate with credit and product teams, develop commercial and analytical expertise, and progressively take ownership of strategic analyses and initiatives.

Lead the technical vision and target-state architecture for large-scale, mission-critical enterprise systems, data platforms, cloud integrations, and AI capabilities. Establish architectural standards, governance, security, resilience, and AI adoption strategies while mentoring senior technical leaders. Partner with executives and engineering teams to translate business objectives into scalable solutions, investigate complex incidents, manage technical debt, and remain hands-on through prototypes and proof-of-concepts.

Collect and analyse open-source online data (including social media) to produce quantitative and qualitative insights, draft analytical reports, maintain databases, present findings to clients, support methodology development and business development, and respond to incidents outside regular hours.

Lead technical development and production deployment of ML models for search, personalization, ranking, and recommendations. Build scalable, explainable ML solutions, own end-to-end impact, collaborate with backend and product teams, and iterate on experiments using cloud-native data at scale.

Design, build, evaluate, and deploy AI-powered software development workflows using Codex. Partner with engineering organizations to move from prototypes to reliable production systems, improve developer adoption and productivity, and address model behavior, integrations, security, reliability, observability, cost, and operational readiness. Lead technical workshops, diagnose implementation challenges, develop evaluation systems, and translate deployment insights into product feedback, reusable architectures, and technical resources.

Produce performance attribution, portfolio analytics and client reporting for institutional multi-asset and single-asset portfolios. Calculate and review performance, produce committee/trustee reports, liaise with custodians and managers, respond to client queries, write investment commentary, and improve processes via automation.

As a Quantitative Researcher, you will develop trading strategies, implement mathematical models, analyze data, and contribute to high-level research in a trading environment.

11-week internship performing quantitative research to develop, backtest, and implement automated trading strategies. Translate mathematical models into code, analyze unconventional data sources, conduct statistical research (ML, time-series, NLP), and collaborate with senior researchers to refine trading signals and monetize models.

Conduct original research applying machine learning, deep learning, and NLP to extract patterns from large and unstructured datasets; implement and back-test algorithms in high-quality code; document findings and collaborate with trading teams to deploy research-driven models.

Design, deploy, and maintain tools and services for build, CI/CD, packaging, and releases. Manage cloud and Kubernetes infrastructure, use IaC, reduce toil, and support junior engineers for reliable ML software delivery.

Develop and maintain internal build, test, and integration tooling for the ML software stack. Build release processes, assist dependency management, guide developers on build best practices, collaborate with QA and engineers to resolve integration issues, and contribute fixes to the ML software stack.

Own a portfolio of enterprise accounts as technical advisor and strategic owner: onboard, enable, drive adoption, troubleshoot integrations, run demos/POCs/workshops, surface requirements for product/engineering, identify expansion, lead executive reviews, and build AI-first automation to remove recurring work.

Lead post-sales technical implementations for enterprise customers of the DX platform, design custom integrations, map platform architecture into customer workflows, run technical workshops, advise on developer experience and deployment best practices, and relay customer technical feedback to Product and Engineering to influence the roadmap.

Lead analytics for customer operations: build capacity planning and forecasting models, own data pipelines, develop predictive and causal inference analyses, track KPIs, standardise processes, and partner with operations, WFM, quality, and training to drive data-driven improvements.