Top Tech Jobs & Startup Jobs in London

Design, develop and optimize a large-scale Monte Carlo engine for CVA/FVA calculations; implement numerical techniques for efficiency; build production-grade analytics; collaborate with traders, risk managers and tech teams; drive software engineering best practices.

Lead end-to-end product lifecycle for lending products within wealth management: define product vision and roadmap, run discovery, own backlog, lead agile teams and releases, mentor product analysts, and use data and design thinking to validate and scale customer-centric solutions.

Lead backend engineering efforts to design, build, operate and secure cloud-native microservices. Deliver reliable, tested, scalable systems, manage stakeholders, coach engineers, support production incidents, and drive adoption of approved AI-assisted development practices.

Lead credit analysis, due diligence, structuring and documentation negotiations for Investment Grade and Leveraged clients across diversified industries. Approve transactions, monitor portfolio credit risk, coach junior staff, and maintain senior stakeholder relationships to balance commercial outcomes with strong risk controls and regulatory interaction.

Lead networking platform engineering for cloud-native systems: design resilient, secure networks on GCP/AWS, build and maintain Kubernetes operators (Golang), improve observability/SLOs, troubleshoot production incidents, drive AI-assisted engineering practices, vendor evaluations, and lead engineering communities and agile ceremonies.

Lead product strategy and roadmap for Global IB Coverage and Global Corporate Banking products. Drive discovery, launches, KPIs, and operational resilience while partnering with engineering, controls, compliance, legal, finance, and GTM teams. Use data-driven insights and emerging technologies like AI to improve product performance and client value.

Lead design and delivery of production-grade LLM and multimodal AI systems. Architect scalable APIs, agentic workflows, and high-throughput microservices. Own end-to-end reliability, performance, and continuous improvement while partnering with ML engineering, product, and cloud teams to deploy secure, enterprise-grade AI at scale.

Lead development and management of agentic AI workbench and control environment, drive integration, deployment, and optimization of agent-led workflows, partner with architecture and engineering, manage risk and KPIs, support stakeholder adoption, and mentor team members to deliver measurable business value across Client360.

Design, build, and productionize autonomous and assistive multi-agent AI systems. Implement RAG and semantic search with vector databases, integrate tools/APIs, apply prompt engineering and guardrails, orchestrate microservices workflows, instrument agents for metrics, run evaluations/A-B tests, and mentor teammates.

Lead design, development, testing, release, and production support for Equity Derivatives workflows. Own production outcomes, incident triage, stakeholder communication, UAT, architecture and observability. Drive adoption of AI-assisted engineering practices, maintain release discipline, gather telemetry for stability metrics, and improve system reliability and coding hygiene through cross-functional collaboration.