We are seeking a Senior Experienced AI Engineer to lead the design, prototyping, and delivery of GenAI-enabled automation solutions across banking operations. This role is intended for a hands-on engineering leader who can translate complex operational workflows into secure, scalable, and auditable AI solutions while operating with the maturity expected of a Vice President-equivalent role in a banking environment.
The successful candidate will combine deep software engineering capability, practical LLM implementation experience, strong stakeholder management, and sound judgment around risk, compliance, data privacy, and model governance. The role requires ownership beyond coding: the engineer will define solution approaches, Operations team, influence business stakeholders, establish engineering standards, and help move high-value prototypes toward pilot and production readiness.
Key Responsibilities- AI solution ownership — Own the end-to-end engineering lifecycle for GenAI and automation initiatives, from process discovery and feasibility assessment through prototype, pilot, production handover, and operational support.
- Banking process automation — Analyse complex banking workflows across operations, compliance, risk, servicing, KYC and reporting to identify high-impact automation opportunities.
- GenAI and agentic system design — Design LLM-powered workflows, RAG-based solutions, autonomous or semi-autonomous agents, prompt chains, tool-calling patterns, and human-in-the-loop review mechanisms.
- Rapid prototyping and MVP delivery — Build functional prototypes and MVPs using leading LLM platforms such as Claude, Gemini, Copilot, OpenAI/Azure OpenAI, or equivalent enterprise AI services.
- Secure integration — Integrate AI capabilities with enterprise systems, APIs, workflow platforms, data repositories, document management systems, and mock or production-like banking data environments.
- Engineering standards — Establish coding, testing, documentation, version control, evaluation, deployment, and observability practices for AI-enabled solutions.
- Responsible AI and governance — Implement guardrails for data privacy, PII handling, hallucination control, bias mitigation, explainability, auditability, cost management, and regulatory compliance.
- Model and prompt evaluation — Define evaluation datasets, success metrics, regression tests, prompt scorecards, failure-mode analysis, and quality thresholds before scaling prototypes.
- Stakeholder leadership — Partner with business, operations, technology, risk, compliance, information security, architecture, and data teams to align solution design with business value and control expectations.
- Mentorship and technical coaching — Guide junior engineers and analysts on prompt engineering, API development, AI implementation patterns, secure coding, and banking-domain solution design.
- 8-10+ years of software engineering, AI engineering, data engineering, platform engineering, or automation delivery experience.
- Hands-on experience building GenAI, LLM, NLP, machine learning, or intelligent automation solutions in enterprise environments.
- Strong programming capability in Python; experience with Java, JavaScript, TypeScript, or similar enterprise development languages is an advantage.
- Practical experience working with LLM platforms such as Claude, Gemini, GitHub Copilot, OpenAI, Azure OpenAI, or equivalent models and AI services.
- Experience designing and integrating REST APIs, SDKs, backend services, microservices, and event-driven or workflow-based architectures.
- Understanding of RAG patterns, vector databases, embeddings, prompt engineering, tool calling, agent orchestration, and LLM evaluation methods.
- Ability to design secure, scalable, auditable, and maintainable solutions suitable for regulated banking or financial services environments.
- Working knowledge of DevSecOps practices, CI/CD pipelines, automated testing, source control, deployment automation, monitoring, and incident management.
- Strong understanding of data privacy, PII protection, encryption, access control, audit logging, and model risk considerations.
- Excellent communication skills, with the ability to explain technical trade-offs, risks, and architecture decisions to senior business and technology stakeholders.
- Prior experience in banking, capital markets, lending, payments, financial operations, compliance, KYC, AML, risk, or regulatory reporting.
- Experience with cloud platforms such as Microsoft Azure, Google Cloud Platform, or AWS, including secure deployment of AI or data workloads.
- Exposure to Lang Chain, Llama Index, Semantic Kernel, agent frameworks, orchestration tools, vector stores, and enterprise knowledge search patterns.
- Experience with Docker, Kubernetes, Infrastructure-as-Code, environment provisioning, observability tools, and production support practices.
- Knowledge of model governance, responsible AI policies, human-in-the-loop design, AI risk reviews, and financial audit expectations.
- Ability to create solution design documents, architecture diagrams, risk assessments, operating models, runbooks, and executive-ready updates.
- Experience influencing senior stakeholders, leading workshops, facilitating discovery sessions, and translating ambiguous business problems into actionable solution backlogs.
- Bachelor’s or master’s degree in computer science, Engineering, Data Science, Artificial Intelligence, Information Systems, or a related discipline; equivalent practical experience will also be considered.
- Demonstrated experience delivering enterprise-grade technology solutions, preferably in banking, financial services, insurance, fintech, or another regulated environment.
- Evidence of hands-on AI or automation delivery through prototypes, production solutions, repositories, architecture artifacts, case studies, or demos.
- Ability to operate independently with limited supervision, manage competing priorities, and take accountability for solution quality, risk, and delivery outcomes.
This is not a purely individual contributor role. The candidate is expected to demonstrate VP-equivalent ownership by managing ambiguity, influencing senior stakeholders, guiding technical decisions, mentoring junior resources, and ensuring every AI solution is aligned to business value, engineering discipline, operational resilience, and regulatory expectations.
#LI-NC2
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Job Family Group: Operations - Services------------------------------------------------------
Job Family:Business KYC------------------------------------------------------
Time Type:Full time------------------------------------------------------
Most Relevant Skills Business Acumen, Credible Challenge, Laws and Regulations, Management Reporting, Policy and Procedure, Program Management, Referral and Escalation, Risk Controls and Monitors, Risk Identification and Assessment, Risk Remediation.------------------------------------------------------
Other Relevant Skills For complementary skills, please see above and/or contact the recruiter.------------------------------------------------------
Citi is an equal opportunity employer, and qualified candidates will receive consideration without regard to their race, color, religion, sex, sexual orientation, gender identity, national origin, disability, status as a protected veteran, or any other characteristic protected by law.
If you are a person with a disability and need a reasonable accommodation to use our search tools and/or apply for a career opportunity review Accessibility at Citi.
View Citi’s EEO Policy Statement and the Know Your Rights poster.
Skills Required
- 8-10+ years of software engineering, AI engineering, data engineering, platform engineering, or automation delivery experience
- Hands-on experience building GenAI, LLM, NLP, machine learning, or intelligent automation solutions in enterprise environments
- Strong programming capability in Python
- Experience with LLM platforms such as Claude, Gemini, GitHub Copilot, OpenAI, Azure OpenAI, or equivalent
- Experience designing and integrating REST APIs, SDKs, backend services, microservices, and event-driven or workflow-based architectures
- Understanding of RAG patterns, vector databases, embeddings, prompt engineering, tool calling, and agent orchestration
- Ability to design secure, scalable, auditable, and maintainable solutions suitable for regulated banking or financial services
- Working knowledge of DevSecOps practices, CI/CD pipelines, automated testing, source control, deployment automation, monitoring, and incident management
- Strong understanding of data privacy, PII protection, encryption, access control, audit logging, and model risk considerations
- Excellent communication skills to explain technical trade-offs, risks, and architecture decisions to senior stakeholders
- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, AI, Information Systems, or equivalent practical experience
- Evidence of hands-on AI or automation delivery through prototypes, production solutions, repositories, architecture artifacts, case studies, or demos
- Prior experience in banking, capital markets, lending, payments, financial operations, compliance, KYC, AML, risk, or regulatory reporting
- Experience with cloud platforms such as Azure, GCP, or AWS for secure deployment of AI or data workloads
- Exposure to LangChain, LlamaIndex, Semantic Kernel, agent frameworks, orchestration tools, and vector stores
- Experience with Docker, Kubernetes, Infrastructure-as-Code, environment provisioning, observability tools, and production support
- Knowledge of model governance, responsible AI policies, human-in-the-loop design, AI risk reviews, and financial audit expectations
- Ability to create solution design documents, architecture diagrams, risk assessments, operating models, runbooks, and executive updates
- Experience influencing senior stakeholders, leading workshops, and translating ambiguous business problems into solution backlogs
Citi Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Citi and has not been reviewed or approved by Citi.
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Healthcare Strength — Benefits coverage is positioned as comprehensive, including health, dental, and vision insurance plus on-site clinics, prescription drug support, and disability coverage. Family-building support such as fertility assistance is described as a notable differentiator within the overall package.
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Retirement Support — Retirement benefits are framed as strong, highlighted by a 401(k) with matching and additional plan options like a Roth 401(k). Financial support is reinforced through discounts and broader financial guidance resources tied to the benefits ecosystem.
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Wellbeing & Lifestyle Benefits — Wellbeing support extends beyond insurance through programs like an Employee Assistance Program, counseling/legal resources, and gym or wellness reimbursement. These offerings increase the perceived total rewards value even when cash compensation sentiment varies by role.
Citi Insights
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