Application Development Group Manager - Senior Vice President

Citi•Rutherford, NJ

3d•$176,720 - $265,080

About The Position

Citi is seeking a senior technology leader to lead its Retail and Wealth Risk Engineering organization within Enterprise Risk Technology. The role is responsible for defining strategy, architecture, and execution for enterprise-scale AI platforms, Java/Python engineering ecosystems, data platforms, and full-stack solutions. The leader will manage product-based development lifecycle (PDLC) teams, including human engineers and AI-assisted or virtual developers, and advance AI PDLC, regulatory-compliant data platforms, cloud-native engineering, and scalable GenAI solutions aligned with Retail Risk, Wealth Risk, and regulatory programs such as CCAR and FDIC. The position serves as a senior architecture and engineering authority across AI agent platforms, full-stack applications, data engineering, and microservices. It leads the delivery and governance of AI and data systems, ensuring scalability, resilience, security, maintainability, data quality, lineage, observability, auditability, and regulatory compliance. The role partners with Risk, Finance, Data, Compliance, and Retail Banking stakeholders to translate business and regulatory needs into scalable engineering solutions, while setting standards and mentoring engineering leaders and teams. The position is based in Rutherford, New Jersey, and is full-time. The listed salary range is $176,720 to $265,080. Citi notes that eligible employees may also receive incentive and retention awards and offers medical, dental, and vision coverage; 401(k); life, accident, and disability insurance; wellness programs; paid vacation, sick leave, and holidays. Benefits may vary by jurisdiction, job level, and hire date.

Requirements

Bachelor’s degree, university degree, or equivalent experience.
Experience leading product-based development lifecycle teams and Agile Scrum teams of engineers.
Expertise in Java/Python engineering, AI platforms, data platforms, full-stack solutions, and enterprise architecture.
Experience with AI Product Development Lifecycle, model governance, evaluation, deployment, monitoring, and Model Risk Management.
Experience with high-volume data pipelines, data federation, Spark, PySpark, Databricks, Kafka, and Data Mesh architecture.
Experience with enterprise AI agents, LLM platforms, Agentic AI, Human-in-the-Loop controls, and GenAI solutions.
Experience integrating AI-assisted development tools and applying prompt engineering and governance guardrails.
Experience with microservices, Spring Boot, FastAPI, React, Angular, Kubernetes/OpenShift, and CI/CD pipelines.
Ability to ensure scalability, resilience, security, maintainability, data quality, lineage, observability, auditability, and regulatory compliance.
Ability to work with Risk, Finance, Data, Compliance, and Retail Banking stakeholders and translate business and regulatory requirements into engineering solutions.
Ability to establish engineering standards and mentor engineering managers, architects, and senior engineers.
Ability to assess and escalate risk and control issues and ensure compliance with applicable laws, rules, and regulations.

Nice To Haves

Exposure to Retail Lending, Credit Risk, Wealth Risk, and regulatory platforms, including CCAR (14Q/14A/14M), FDIC reporting, and enterprise risk aggregation.
Deep core systems expertise in Retail Banking domains such as Cards, Mortgages, Consumer Lending, Wealth Lending, Deposits, Finance, and Risk Data Platforms.
Business domain experience working directly with Retail Banking, Wealth Management, Risk, Finance, and Data organizations, including knowledge of business processes, architecture, and infrastructure.
Experience with Docker, Kubernetes, cloud-native engineering, and DevSecOps practices.
Knowledge of AI PDLC, Responsible AI, Model Risk Management, and model governance frameworks.
Experience implementing enterprise-scale AI agents, GenAI, Retrieval Augmented Generation (RAG), Agentic AI frameworks, and Human-in-the-Loop controls.
Experience with Data Mesh, Data Products, Data Federation, Metadata Management, Data Catalogs, and enterprise data governance programs.
Exposure to AWS, Azure, GCP, Databricks, and modern enterprise engineering ecosystems.
Relevant AWS, Azure, GCP, Kubernetes, Databricks, AI/ML, Architecture, or Cloud Engineering certifications.

Responsibilities

Lead multiple Agile Scrum teams, including teams of human engineers and AI-assisted developers, and ensure delivery excellence and alignment with business priorities.
Define and execute enterprise strategy for Java/Python engineering, AI agent platforms, full-stack applications, and data platforms aligned with Retail and Wealth Risk objectives.
Serve as senior architect and technical authority for enterprise-scale AI agents, full-stack applications, data engineering platforms, and microservices-based solutions.
Drive adoption and operationalization of AI Product Development Lifecycle (AI PDLC), including model governance, evaluation, deployment, monitoring, and Model Risk Management compliance.
Lead development of high-volume data pipelines and data federation layers using Spark, PySpark, Databricks, Kafka, and Data Mesh architecture for regulatory reporting and risk analytics.
Architect and oversee GenAI agent ecosystems using enterprise LLM platforms, Agentic AI frameworks, and Human-in-the-Loop controls, ensuring explainability, auditability, and compliance.
Integrate AI-assisted development tools such as Devin.AI, GitHub Copilot, and MCP platforms into the software development lifecycle using prompt engineering and governance guardrails.
Lead microservices and cloud-native architecture using Spring Boot, FastAPI, React, Angular, Kubernetes/OpenShift, and CI/CD pipelines.
Improve engineering efficiency and standardization by reusing enterprise frameworks, platforms, and tools to reduce duplication and accelerate delivery.
Ensure solutions meet data governance and non-functional requirements, including data quality, lineage, tracing, observability, resiliency, and auditability.
Partner with Risk, Finance, Data, Compliance, and Retail Banking stakeholders to translate regulatory and business requirements into engineering solutions.
Build relationships with senior business leaders and lead strategic discussions, requirement gathering, roadmap planning, and cross-functional alignment.
Establish engineering standards, governance frameworks, architecture patterns, and best practices across teams.
Mentor engineering managers, architects, and senior engineers, fostering innovation, accountability, AI adoption, and continuous improvement.
Assess risk in business decisions, safeguard Citi, its clients, and assets, comply with applicable laws and regulations, and transparently escalate control issues.

Benefits

Medical insurance
Dental insurance
Vision insurance
401(k)
Life insurance
Accident insurance
Disability insurance
Wellness programs
Paid vacation
Paid sick leave
Paid holidays
Eligible employees may receive discretionary and formulaic incentive and retention awards.