Senior Engineer/Engineering Lead - Senior Vice President
Citigroup
- Location
- London United Kingdom
- Work model
- On-Site
- Level
- Staff
- Salary
- $5/hr
- Posted
- Aug 21, 2026
Skills
About this role
Engineer the future of global finance. At Citi, our Tech team doesn’t just support finance – we are helping to redefine it. Every day, $5 trillion crosses through our network. We do business in 180+ countries operating at a scale few can match. From deploying advanced AI to helping shape global markets, we build systems that matter. Look to join a team where your work helps influence economies, your ideas can drive innovation and outcomes, and your growth is backed by mentorship, continuous learning and flexibility with potential hybrid work opportunities. Help solve real-world challenges that touch millions and get the opportunity to build the future of finance with Citi Tech.
About the Team
The Enterprise AI Products team is a small, high-impact global team within the Citi COO/CTO organisation, dedicated to building innovative AI solutions for the wider bank. We are at the forefront of Generative AI, delivering capabilities that enable safe and efficient use of Generative AI at scale. We create services, applications and libraries that power the use of Intelligent Document Processing to drive real-world business impact.
The Role
This is a senior role within a deliberately flat team structure. You will be expected to take ownership, bring fresh thinking, and collaborate closely with colleagues across disciplines to deliver outcomes that genuinely make a difference. You will write bulletproof, production-grade code — not prototypes — to solve novel problems that sit at the frontier of applied Generative AI. Typical problem spaces you'll work on include intelligent data extraction from unstructured and semi-structured documents, content classification and routing, cross-language translation, fraud and document-tampering detection, and workflow optimisation across business processes. You'll be trusted to turn ambiguous, often first-of-a-kind problems into dependable services that the business can run its operations on. Because these solutions operate in a regulated banking environment, resilience, explainability, and auditability are as important as raw model performance — and you will move fluidly between experimentation, evaluating models, prompts, and architectures, and hardening the winning approach into dependable, scalable production code. You will gain meaningful exposure to AI development and solutions, alongside a broad range of technical work in a fast-moving, delivery-focused environment.
Responsibilities
Own the design and build of high-quality, reliable software that scales. Own, lead, and manage technical components end-to-end, ensuring alignment with business objectives and the wider product strategy. Work with partners in Risk and other AI platform teams to help evolve Citi technology and procedures. Partner with senior stakeholders to shape a compelling vision and technical roadmap for our products. Provide technical guidance and mentorship to junior team members, elevating the team's overall capability. Ensure software quality through rigorous code reviews, technical design reviews, and a strong testing culture.
What We're Looking For
Significant experience as a senior developer, technical lead, or similar role. Working alongside other teams to ensure that solutions work seamlessly across business verticals Extensive experience crafting prompts across a variety of Generative AI models — you have strong, well-formed opinions on prompt structure and clarity, approaching it with the same rigor as The Elements of Style brings to writing. Creative mindset –the problems we’re solving often require novel solutions but are constrained by regulatory and compliance requirements. Experience building production systems for one or more of: document/data extraction, NLP-based classification, machine translation, fraud or anomaly detection, or workflow automation. Experience evaluating the performance of Generative AI applications – exposure to Mlflow, experiment design, evaluation methodologies.