VP, Applications Development – Tech Lead (Java, UI, PL-SQL & Agentic AI)

📍 Job Overview

Job Title: VP, Applications Development – Tech Lead (Java, UI, PL-SQL & Agentic AI)

Company: Citi

Location: Tampa, Florida, United States (Primary); Irving, Texas, United States (Secondary)

Job Type: Full time

Category: Technology / Applications Development / Generative AI

Date Posted: 2026-08-25

Experience Level: 5-10 Years (Mid to Senior Level)

Remote Status: Hybrid

🚀 Role Summary

• Lead the design, development, and integration of state-of-the-art Generative AI and agentic AI solutions within the Controls Technology platform.

• Architect and implement advanced context engineering strategies to maximize reliability, provenance, and token efficiency for production AI systems.

• Drive the development of sophisticated Retrieval-Augmented Generation (RAG) systems and knowledge graph integrations for enhanced reasoning and explainability.

• Design and build agentic workflows and multi-agent systems using leading frameworks like Google Agent Development Kit (ADK).

• Ensure the robust deployment, scalability, observability, and maintainability of AI applications in production environments.

📝 Enhancement Note: This role is highly specialized, focusing on the application of Generative AI and agentic AI rather than model training or fine-tuning. The emphasis is on leveraging existing foundation models to build complex, orchestrated AI solutions for enterprise use cases. The "VP" title suggests a significant leadership and technical architect role, requiring deep expertise and the ability to mentor.

📈 Primary Responsibilities

• Architect and implement advanced context engineering strategies, including context layering, chaining, compression, pruning/offloading, and memory management.

• Design and develop sophisticated Retrieval-Augmented Generation (RAG) systems, incorporating hybrid search, multi-vector retrieval, and re-ranking pipelines.

• Build and optimize knowledge graphs and Graph RAG architectures to enable multi-hop reasoning, explainability, and traceable, grounded responses.

• Design and implement agentic workflows and multi-agent systems using frameworks such as Google Agent Development Kit (ADK), LangGraph, Microsoft Agent Framework, or CrewAI.

• Architect robust agent harnesses encompassing governance, constraints, feedback loops, state/session management, and execution controls.

• Integrate agents with tools and data via the Model Context Protocol (MCP) and orchestrate inter-agent collaboration via the Agent2Agent (A2A) protocol.

• Support the integration of Generative AI and agentic applications into production environments, focusing on deployment, scalability, observability, and maintainability.

• Contribute to the development and optimization of real-time and streaming AI solutions.

• Stay current with the latest advances in generative and agentic AI, sharing knowledge and fostering innovation within the team.

• Ensure strict adherence to ethical AI guidelines, guardrails, agent isolation/sandboxing, data privacy, and compliance standards.

• Mentor junior team members, conduct code reviews, and promote a culture of technical excellence and responsible AI development.

📝 Enhancement Note: The responsibilities highlight a need for deep technical leadership in applied AI. The emphasis on specific protocols like MCP and A2A, along with frameworks like Google ADK, indicates a focus on building interoperable and robust agent systems. The mention of "Controls Technology platform" suggests the applications will be critical for financial operations, compliance, or risk management.

🎓 Skills & Qualifications

Education: Bachelor's or Master's degree in Computer Science, Data Science, Artificial Intelligence, or a closely related technical field.

Experience: 5–7 years of progressive experience in AI/software development, with a significant portion dedicated to hands-on work in Generative AI and agentic AI.

Required Technical Skills:

• Deep, hands-on expertise in core generative AI concepts: foundation models, LLMs, embeddings, tokenization, and context-window management.

• Advanced skills in prompt engineering and context engineering, including dynamic context orchestration and prompt design tools.

• Strong experience building Retrieval-Augmented Generation (RAG) systems, including chunking strategies, hybrid search, and multi-vector retrieval.

• Practical experience designing knowledge graphs and Graph RAG pipelines (e.g., using Neo4j or ArangoDB) for relationship-aware, multi-hop retrieval.

• Proven experience building agentic AI systems with Google ADK and/or comparable frameworks (LangGraph, Microsoft Agent Framework, CrewAI, OpenAI Agents SDK), including tool/function calling, planning, and memory management.

• Strong grasp of multi-agent orchestration patterns (supervisor/worker, hierarchical, peer-to-peer) and harness engineering (governance, feedback loops, execution controls, agent isolation/sandboxing).

• Hands-on experience with agent interoperability protocols: Model Context Protocol (MCP) for tool/data access and Agent2Agent (A2A) for inter-agent collaboration.

• Experience with agent observability and evaluation tools (e.g., tracing, OpenTelemetry-based tooling).

• Proficiency with major GenAI APIs (OpenAI, Gemini, Claude) and orchestration frameworks like LangChain and LlamaIndex.

• Strong skills in Natural Language Processing (NLP) techniques such as Named Entity Recognition (NER), dependency parsing, text classification, and topic modeling.

• Proficiency with vector databases and embedding models for large-scale retrieval.

• Experience with containerization (Docker), orchestration (Kubernetes), and CI/CD pipelines for AI/agentic applications.

• Solid understanding of AI compliance, guardrails, and responsible AI practices.

• Strong proficiency in Python for data preprocessing, document ingestion, and API development.

• Familiarity with traditional enterprise development stacks, including Java, UI frameworks, and PL/SQL, may be beneficial for integration purposes. Preferred Skills:

• Experience working with AWS (or equivalent) cloud infrastructure for AI/GenAI deployment and management.

• Familiarity with existing enterprise application development and database technologies (Java, UI, PL-SQL) for seamless integration.

• Experience with real-time and streaming AI solutions.

• Demonstrated portfolio of successful AI-driven projects in a business environment.

📝 Enhancement Note: The required technical skills list is extensive and highly specific to modern AI development. While the job title includes Java, UI, and PL-SQL, the core responsibilities and required skills heavily lean towards Generative AI and agentic AI development using Python. It's crucial for candidates to showcase deep expertise in the AI-specific technologies. The "VP" title implies a senior technologist who can also guide and influence technical direction.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrable case studies showcasing the design and implementation of complex Generative AI solutions, particularly those involving RAG, knowledge graphs, or agentic workflows.

• Evidence of architecting and building robust agent harnesses with clear governance, control mechanisms, and integration strategies.

• Examples of implementing agent interoperability protocols (MCP, A2A) and utilizing frameworks like Google ADK or LangGraph.

• Documentation or presentations detailing successful integration of AI/agentic applications into production environments, highlighting scalability, observability, and maintainability.

• Projects demonstrating advanced context engineering strategies and their impact on AI model performance and efficiency. Process Documentation:

• Workflow diagrams and technical documentation for designed AI solutions, illustrating agent orchestration, data flow, and system architecture.

• Code repositories (e.g., GitHub) showcasing clean, well-documented Python code for AI/agentic applications, including unit tests and integration tests.

• Documentation of CI/CD pipelines established for AI/agentic application deployment and management.

• Process descriptions for ensuring responsible AI practices, including guardrails, compliance checks, and agent isolation strategies.

• Examples of how performance metrics and observability data were used to optimize AI systems post-deployment.

📝 Enhancement Note: For a VP-level Tech Lead role, the portfolio should not just list projects but demonstrate architectural vision, problem-solving prowess in complex AI scenarios, and the ability to build production-ready, scalable, and secure AI systems. The focus is on the how and why behind the technical decisions, with clear articulation of business impact and technical trade-offs.

💵 Compensation & Benefits

Salary Range: $113,840.00 - $170,760.00 USD per year.

Explanation of Range: This range is provided by Citi and reflects the typical compensation for a Vice President (VP) level role in Technology Applications Development, specifically for a specialized Tech Lead in Generative AI. The exact salary within this range will depend on the candidate's experience, skills, qualifications, and the specific location (Tampa or Irving). This aligns with senior-level AI development roles in major financial institutions.

Benefits:

• Comprehensive Medical, Dental, and Vision coverage.

• 401(k) retirement savings plan.

• Life, Accident, and Disability insurance.

• Wellness programs designed to support employee health and well-being.

• Generous Paid Time Off (PTO) package, including vacation days, sick leave, and paid holidays.

• Potential for discretionary and formulaic incentive and retention awards, common for VP-level roles in financial services.

Working Hours: 40 hours per week.

Work Arrangement: Hybrid. Specific in-office days may vary but are expected for collaboration and team engagement.

📝 Enhancement Note: The provided salary range is a full-time primary location salary. Given the specialized nature of AI development and the VP title, candidates in the higher end of this range would be expected, especially those with extensive experience in agentic AI and large-scale deployments. The benefits package is typical for a large, established financial institution like Citi, offering robust support for employees.

🎯 Team & Company Context

🏢 Company Culture

Industry: Financial Services (Banking & Financial Technology). Citi operates at the intersection of traditional banking and technological innovation, leveraging AI to enhance security, compliance, and operational efficiency.

Company Size: Large Enterprise (over 10,000 employees). This means extensive resources, established processes, and a global reach, but also a structured environment.

Founded: 1812. Citi has a long history, indicating stability and a deep understanding of the financial markets, now integrating cutting-edge AI technologies.

Team Structure:

• The role sits within the Controls Technology platform, likely a dedicated engineering team focused on building and maintaining systems that ensure compliance, security, and operational integrity across Citi's vast operations.

• This team will likely include AI architects, Generative AI developers, data scientists, software engineers, and potentially product managers.

• As a VP Tech Lead, you will report into a Director or Senior Director level manager within Controls Technology and will be expected to guide and mentor a team of engineers.

• Collaboration will be extensive, involving cross-functional teams across technology, risk, compliance, and business units to understand needs and integrate AI solutions effectively. Methodology:

• Data-Driven Development: Decisions will be based on rigorous data analysis, performance metrics, and AI model evaluations.

• Agile & Iterative Processes: Expect to work in an agile environment, with iterative development cycles for AI solutions, focusing on rapid prototyping and continuous improvement.

• DevOps & MLOps Practices: Strong emphasis on robust deployment, continuous integration/continuous delivery (CI/CD), and operational excellence for AI and agentic applications.

• Responsible AI Frameworks: Adherence to strict ethical guidelines, guardrails, and compliance standards will be paramount in all development activities.

Company Website: https://www.citi.com/

📝 Enhancement Note: Working at Citi in a VP role means contributing to a critical function within a global financial institution. The Controls Technology platform is vital for managing risk and ensuring regulatory compliance, making the impact of AI solutions here significant. The culture likely balances innovation with a strong emphasis on stability, security, and regulatory adherence.

📈 Career & Growth Analysis

Operations Career Level: Vice President (VP) - Technical Lead. This position represents a senior individual contributor and technical leadership role. It requires deep technical expertise, architectural design capabilities, and the ability to influence technical direction and mentor junior engineers. The focus is on hands-on technical leadership rather than direct people management, though mentoring is a key component.

Reporting Structure: You will report to a senior leader within the Controls Technology organization, likely a Director or Senior Director. You will collaborate closely with AI architects, product owners, and other engineering leads across various business and technology functions.

Operations Impact: This role has a direct impact on enhancing operational efficiency, improving risk management, and ensuring compliance through the implementation of advanced AI technologies. By developing and deploying cutting-edge agentic AI and Generative AI solutions, you will contribute to the modernization and security of Citi's core control systems, thereby safeguarding the company and its clients.

Growth Opportunities:

• Technical Specialization: Deepen expertise in emerging areas of Generative AI, agentic AI, multi-agent systems, and related AI infrastructure.

• Architectural Leadership: Advance into higher-level architectural roles, leading the design of enterprise-wide AI strategies and platforms.

• Cross-Functional Influence: Gain broader exposure to different business units and technologies within Citi, potentially leading to opportunities in product management or broader technology leadership.

• Mentorship & Team Building: Develop leadership skills by mentoring junior engineers and contributing to the growth of the AI talent pool within Citi.

• Industry Recognition: Contribute to industry best practices and potentially present findings at conferences through Citi's engagement in the AI community.

📝 Enhancement Note: The VP title signifies a strategic technical role. Growth will likely involve taking on more complex architectural challenges, leading larger initiatives, and potentially moving into management roles if desired, though the primary focus is on technical leadership. The impact on controls and compliance within a major financial institution offers significant career development.

🌐 Work Environment

Office Type: Hybrid. This role requires a blend of remote work and in-office presence for collaboration, team meetings, and strategic discussions.

Office Location(s): Primary location is Tampa, Florida (3800 Citigroup Center Drive, Building G), with potential for work in Irving, Texas. These are significant corporate campuses offering modern amenities.

Workspace Context:

• Collaborative Spaces: Access to modern office spaces designed for collaboration, including meeting rooms equipped for hybrid work, brainstorming areas, and team zones.

• Technology Infrastructure: Expect robust IT infrastructure, high-speed connectivity, and access to necessary development tools, cloud resources (AWS), and enterprise-grade software.

• Team Interaction: Opportunities for frequent interaction with a diverse team of AI specialists, software engineers, and stakeholders, fostering a dynamic and intellectually stimulating environment.

Work Schedule: Standard business hours (approximately 40 hours per week) with flexibility expected. Given the nature of technology development and potential production support needs, occasional extended hours or weekend work may be required, though this is typically managed through on-call rotations or project planning.

📝 Enhancement Note: The hybrid model at Citi is designed to balance the flexibility of remote work with the benefits of in-person collaboration. For a VP Tech Lead role, being present in the office for key meetings, design sessions, and team interactions is crucial for effective leadership and mentorship.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A recruiter or hiring manager will conduct an initial screening to assess basic qualifications and alignment with the role.

• Technical Interviews (Multiple Rounds): Expect in-depth technical interviews focusing on Generative AI concepts, agentic AI architecture, RAG systems, knowledge graphs, prompt/context engineering, Python proficiency, and experience with relevant frameworks (LangChain, LlamaIndex, ADK, etc.). These may include whiteboard sessions, coding challenges, and system design discussions.

• System Design / Architectural Challenge: A significant portion of the interview process will likely involve a system design or architectural challenge related to building a complex agentic AI solution for a financial controls scenario. You'll be expected to present your approach, discuss trade-offs, and justify your technical decisions.

• Behavioral & Leadership Interviews: Assess soft skills, leadership potential, problem-solving approach, collaboration style, and cultural fit within Citi's values. Questions will likely probe your experience mentoring teams and navigating complex technical challenges.

• Hiring Manager / Senior Leadership Interview: A final discussion to gauge overall fit, strategic thinking, and alignment with the VP level expectations.

Portfolio Review Tips:

• Curate Select Projects: Focus on 2-3 of your most impactful AI/agentic AI projects that best demonstrate your expertise in RAG, knowledge graphs, multi-agent systems, and context engineering.

• Structure Case Studies: For each project, clearly articulate: the business problem, your role and technical approach, the specific AI/agentic technologies and frameworks used, the architectural design, key challenges overcome, and quantifiable results (e.g., efficiency gains, accuracy improvements, cost savings).

• Highlight Architectural Depth: Emphasize your design decisions, trade-offs considered, and how you ensured scalability, reliability, and security. For agentic systems, showcase the harness design, orchestration patterns, and interoperability.

• Code Quality: Be prepared to discuss code quality, testing strategies, and MLOps/CI/CD practices if you have them in your portfolio.

• Tailor to Citi: Research Citi's business and Controls Technology focus. Frame your portfolio examples to show how your skills can directly benefit their operations and risk management objectives.

Challenge Preparation:

• Deep Dive into Frameworks: Thoroughly review documentation and examples for Google ADK, LangGraph, LangChain, LlamaIndex, and relevant vector databases/graph databases.

• Practice System Design: Work through common AI system design problems, focusing on how to integrate LLMs, RAG, and agentic components effectively and reliably.

• Scenario-Based Problem Solving: Anticipate scenarios related to financial services, such as fraud detection, compliance monitoring, or customer service automation, and outline how you would build agentic solutions.

• Articulate Trade-offs: Be ready to discuss the pros and cons of different architectural choices, model selections, and implementation strategies.

• Ethical AI Considerations: Prepare to discuss how you would incorporate guardrails, data privacy, and responsible AI principles into your solutions.

📝 Enhancement Note: The interview process will be rigorous, designed to assess deep technical expertise and leadership potential. Candidates should be prepared to demonstrate not just theoretical knowledge but practical, hands-on experience in building complex, production-grade AI systems. The portfolio review is a critical component for showcasing this practical application.

🛠 Tools & Technology Stack

Primary Tools:

• Generative AI & Orchestration: OpenAI API, Gemini API, Claude API, LangChain, LlamaIndex, Google Agent Development Kit (ADK).

• Agentic AI Frameworks: LangGraph, Microsoft Agent Framework, CrewAI, OpenAI Agents SDK.

• Knowledge Graphs & Graph Databases: Neo4j, ArangoDB, graph traversal languages (e.g., Cypher).

• Vector Databases: Pinecone, Weaviate, Milvus, ChromaDB, FAISS.

• Programming Language: Python (primary for AI development).

• Containerization & Orchestration: Docker, Kubernetes.

• CI/CD Tools: Jenkins, GitLab CI, GitHub Actions, Azure DevOps.

• Cloud Platforms: AWS (preferred), with potential exposure to GCP or Azure.

Analytics & Reporting:

• Observability & Tracing: OpenTelemetry, Prometheus, Grafana, distributed tracing tools.

• Data Analysis Libraries: Pandas, NumPy.

• Visualization Tools: Matplotlib, Seaborn, Plotly (for internal analysis).

CRM & Automation:

• While not directly client-facing CRM, the role involves integrating AI with enterprise systems that manage data and workflows. Understanding of API integrations and data pipelines is key.

• Integration Technologies: RESTful APIs, message queues (e.g., Kafka, RabbitMQ).

• Traditional Enterprise Tech (for context): Java, UI frameworks, PL/SQL, SQL databases.

📝 Enhancement Note: The technology stack is heavily skewed towards modern AI development tools and frameworks. Proficiency in Python is paramount. While the job title mentions Java, UI, and PL-SQL, the core AI responsibilities emphasize Python-based AI development. Experience with cloud infrastructure (AWS) and containerization (Docker, Kubernetes) is essential for deploying and managing these applications at scale.

👥 Team Culture & Values

Operations Values:

• Innovation & Excellence: A drive to push the boundaries of AI technology while maintaining high standards for code quality, performance, and reliability.

• Collaboration & Knowledge Sharing: An environment where team members actively collaborate, share insights, and mentor each other to foster collective growth.

• Data-Driven Decision Making: A commitment to using data and rigorous analysis to inform design choices, measure impact, and drive continuous improvement.

• Integrity & Responsibility: A strong emphasis on ethical AI practices, data privacy, security, and compliance, especially critical within the financial services industry.

• Efficiency & Automation: A focus on leveraging AI and automation to streamline processes, reduce operational costs, and enhance efficiency across the enterprise.

Collaboration Style:

• Cross-Functional Integration: Close collaboration with product managers, business stakeholders, risk managers, and compliance officers to ensure AI solutions meet real-world needs and regulatory requirements.

• Agile Teamwork: Working in agile sprints, participating in daily stand-ups, sprint planning, and retrospectives to ensure alignment and rapid iteration.

• Open Communication: Encouraging open dialogue, constructive feedback, and a proactive approach to problem-solving.

• Mentorship Culture: A willingness to share expertise, guide junior team members, and foster a learning environment.

📝 Enhancement Note: Citi's culture, especially within a critical area like Controls Technology, will likely emphasize a blend of innovation and a strong adherence to established protocols and ethical standards. The VP Tech Lead role requires someone who can champion new AI approaches while ensuring they are implemented responsibly and align with the company's robust risk and compliance framework.

⚡ Challenges & Growth Opportunities

Challenges:

• Complexity of Agentic Systems: Designing, building, and debugging multi-agent systems that interact reliably and safely at scale presents significant technical challenges.

• Ensuring AI Reliability & Safety: Implementing robust guardrails, error handling, and self-correction mechanisms for AI applications, especially in a high-stakes financial environment.

• Integration with Legacy Systems: Seamlessly integrating cutting-edge AI solutions with existing enterprise infrastructure, which may include older technologies.

• Staying Ahead of AI Advancements: The rapid pace of AI development requires continuous learning and adaptation to new models, frameworks, and techniques.

• Data Privacy & Security: Navigating strict data privacy regulations and ensuring the security of sensitive financial data used by AI models.

Learning & Development Opportunities:

• Advanced AI Specialization: Opportunities to deepen expertise in areas like multi-agent coordination, advanced RAG techniques, explainable AI (XAI), and AI governance.

• Industry Conferences & Training: Access to leading AI conferences, workshops, and specialized training programs to stay at the forefront of the field.

• Cross-Domain Exposure: Gaining insights into various financial control functions (e.g., fraud detection, compliance, AML) and how AI can be applied across them.

• Leadership Development: Opportunities to develop leadership and mentorship skills, guiding technical teams and influencing strategic direction.

• Internal Mobility: Potential for growth into broader architectural roles, AI strategy leadership, or management positions within Citi's global technology organization.

📝 Enhancement Note: This role is at the cutting edge of AI application development. The challenges are significant but come with substantial opportunities for professional growth and impact. Candidates should be prepared for a dynamic environment where continuous learning is not just encouraged but essential.

💡 Interview Preparation

Strategy Questions:

• "Describe a complex agentic AI system you designed or significantly contributed to. What were the key architectural decisions, challenges, and outcomes?"

• "How would you architect a RAG system for sensitive financial data, ensuring both accuracy and data privacy, while optimizing for token efficiency?"

• "Explain your approach to building and managing multi-agent workflows. What orchestration patterns have you used, and what are their trade-offs?"

• "How do you ensure the reliability, safety, and ethical compliance of Generative AI applications in a regulated industry like finance?"

• "Walk me through your process for implementing agent interoperability protocols (MCP, A2A) and integrating agents with external tools." Company & Culture Questions:

• "Why are you interested in applying advanced AI technologies specifically within Citi's Controls Technology platform?"

• "How do you approach mentoring junior engineers on complex AI projects?"

• "Describe a time you had to balance innovation with strict regulatory requirements in a technology project."

• "How do you stay current with the rapidly evolving field of Generative AI and agentic AI?"

• "What are your thoughts on responsible AI development, and how do you integrate these principles into your work?" Portfolio Presentation Strategy:

• Focus on Impact: For each project, clearly articulate the business problem, your specific contribution, the technical solution, and the measurable impact (e.g., % improvement in efficiency, reduction in false positives, cost savings).

• Showcase Architecture: Use diagrams to illustrate your system designs, especially for agentic workflows, RAG pipelines, and knowledge graph integrations. Explain the reasoning behind your choices.

• Demonstrate Technical Depth: Be ready to dive deep into the code, algorithms, and specific tools/frameworks you used. Explain how you overcame technical hurdles.

• Highlight Collaboration: If possible, showcase how you collaborated with cross-functional teams or stakeholders.

• Practice Your Narrative: Rehearse your presentation to ensure a clear, concise, and compelling story for each project. Be prepared to answer detailed questions about your work.

📝 Enhancement Note: Interviewers will be looking for a blend of deep technical expertise, strategic thinking, practical implementation experience, and leadership potential. Candidates should be prepared to articulate their contributions clearly, showcase their understanding of the AI landscape, and demonstrate how they can apply these skills to solve real-world problems at Citi.

📌 Application Steps

To apply for this operations position:

• Submit your application through the provided link on Citi's Workday careers portal.

• Curate Your Portfolio: Select 2-3 key projects that best showcase your expertise in Generative AI, agentic AI, RAG, knowledge graphs, and system architecture. Prepare detailed case studies with clear problem statements, technical solutions, and quantifiable results.

• Optimize Your Resume: Tailor your resume to highlight keywords from the job description, emphasizing your experience with Python, Generative AI frameworks (LangChain, LlamaIndex, ADK), RAG, agentic systems, vector databases, and cloud platforms (AWS). Quantify achievements wherever possible.

• Prepare for Technical Deep Dives: Review core Generative AI concepts, prompt engineering techniques, RAG architectures, agent orchestration patterns, and Python programming. Practice coding challenges and system design scenarios relevant to AI applications.

• Research Citi: Understand Citi's business, its role in financial services, and the importance of its Controls Technology platform. Familiarize yourself with their commitment to responsible AI and innovation.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.