Top Tech Jobs & Startup Jobs in London

Develop and deploy AI and machine learning solutions for workforce analytics. Conduct statistical analyses, experiments, and model evaluations; build scalable model factories and evaluation frameworks; apply generative AI and agentic development approaches; collaborate with business, product, and technology teams; communicate insights; and ensure compliance with risk, control, and governance requirements.

Leads strategy and delivery for a portfolio of HR data products, defining multi-year roadmaps, OKRs, backlogs, and business outcomes. Partners with HR, BI, AI/ML, Information Architecture, and Data Engineering teams to deliver governed, discoverable, high-quality data assets. Oversees data onboarding, metadata, catalog, lineage, quality, compliance, stakeholder discovery, adoption, and continuous improvement for talent acquisition and off-boarding datasets.

Leads technology risk and controls governance for cloud foundational services. Produces executive-ready governance materials, manages meetings and remediation actions, monitors KRIs and KPIs, validates control effectiveness, synthesizes risks into senior-level insights, and improves reporting processes. Partners across functions to strengthen oversight, using authorized AI and automation with appropriate human validation, data sensitivity awareness, and auditability.

Off-cycle quantitative research intern supporting systematic trading, financial engineering, statistical modeling, portfolio optimization, and derivatives pricing. Responsibilities include developing mathematical models and algorithms, valuing and hedging transactions, conducting alpha research, calibrating models, optimizing pricing, managing portfolio risk, and collaborating with traders, technologists, sales, and risk teams. Candidates gain hands-on global markets experience and may be considered for full-time employment after successful completion.

Develop and deploy production AI and machine learning solutions for Markets Operations, including process automation, anomaly detection, document intelligence, workflow optimization, and generative AI. Responsibilities include model development, MLOps, performance monitoring, dataset analysis, stakeholder collaboration, documentation, and continuous improvement of AI-driven business tools.

Lead strategy and delivery for AI and agentic products that automate operational workflows in corporate and investment banking. Identify high-impact use cases, own roadmaps and product requirements, guide cross-functional teams through launch, develop project plans, and align senior stakeholders. The role requires end-to-end AI product management experience, knowledge of LLM architectures and agentic methods, and the ability to connect product execution to measurable business outcomes.

Leads development of high-performance, latency-sensitive trading systems using Java and Python. Oversees architecture, coding, testing, APIs, integration strategies, observability, resilience, security, incident response, and operational readiness. Guides agile delivery through technical planning, code reviews, retrospectives, and mentoring. Establishes responsible use of AI-assisted engineering tools and validates outputs for correctness, performance, and security in a regulated financial environment.

Lead technology risk and controls activities across cybersecurity and business applications. Assess, monitor, and report risks; evaluate controls; identify gaps; recommend mitigation strategies; support regulatory compliance; document issues and action plans; and conduct threat modeling with application teams. The role requires collaboration with senior stakeholders and technology teams, plus knowledge of SDLC, cloud infrastructure, IAM, data protection, application security, resiliency, and vulnerability management.

Leads a team building secure, production-grade AI-driven software systems. Responsibilities include software architecture, Java or Python development, AI/ML integration, LLM and generative AI applications, algorithm validation, automated testing, CI/CD, cloud-native development, troubleshooting, and operational reliability. The role also establishes responsible AI practices, validates AI-assisted engineering outputs, mentors engineers, translates business requirements into technical roadmaps, and ensures timely delivery through Agile methodologies.

Develop and deliver production AI/ML solutions across the full lifecycle using Agile methodologies. Translate business requirements into technical specifications, build secure production code and algorithms, create architecture artifacts, analyze large datasets, develop visualizations, troubleshoot systems, and identify improvements. The role requires expertise in machine learning, Python, Spark, distributed computing, AI frameworks, agentic workflows, and AWS services, while contributing to engineering communities and emerging technology initiatives.