About the Role Citi's Wholesale Technology organization is seeking an exceptional, hands‑onLead Agentic AI Engineer (VP)to design, build, and deploy cutting‑edge agentic AI solutions. This role combinesdeep technical leadershipwith architectural ownership — driving adoption of LLMs, agentic workflows, and generative AI platforms to improve efficiency, automation, and risk reduction across Citi's global banking operations. You will operate with anAI‑first mindset , emphasizing rapid prototyping, MVP‑driven development, and iterative delivery of production‑grade AI capabilities.Key Responsibilities Agentic AI Design & EngineeringLead end‑to‑end design, development, and deployment of large‑scale agentic AI solutions usingGoogle Agent Development Kit (ADK)and frameworks such as LangChain, LangGraphArchitect advancedmulti‑agent systems(perception, reasoning, planning, execution) integrating multiple LLM providers (OpenAI, Anthropic, Google Gemini)Build AI‑powered capabilities usingGoogle Gemini, Vertex AI, Agent Development Kit (ADK), Google A2UI , vector databases, RAG pipelines, semantic search, and advanced prompt and context managementEngineer autonomous agents incorporating planning, tool usage, memory management, and multi‑step reasoning patternsFull‑Stack AI & Backend EngineeringDevelop scalable, high‑performance backend services inPython(FastAPI, asyncio) with resilient APIs, event‑driven designs, and microservices architecturesBuild and maintain robustdata pipelinesworking with SQL (Oracle, PostgreSQL) and NoSQL (MongoDB) databasesImplement secureREST APIs and agent interfaceswith strong authentication, authorization (OAuth), and encryption best practicesOptimize AI agentperformance, latency, and costthrough prompt optimization, caching strategies, and vector index tuningArchitecture, Strategy & Best PracticesProvide architectural guidance forNext‑Generation AI (NGAI)initiatives, ensuring adherence to CTO guidelines and platform standardsDevelop and maintain a strategic roadmap forgenerative AI adoption , evaluating new models, techniques, and platformsEstablish and govern best practices for the full AI development lifecycle: prompt engineering, model evaluation, MLOps, and data managementCI/CD, MLOps & ObservabilityDriveCI/CD practicesintegrating automated testing, agent evaluation, code quality gates, containerization, and cloud‑native deployment pipelinesAutomate AI model quality, performance testing, and MLOps build processing in the CI/CD pipelineLeadership, Mentorship & CollaborationMentor AI/ML Engineers on best practices in agentic AI development, Google ADK, and advanced AI technologiesChampionMVP‑driven delivery , rapid iteration, and A/B experimentation to achieve fast time‑to‑valueCollaborate with business units to identify high‑impact use cases and ensure AI solutions meet business goalsRequired Qualifications & Skills Experience6–10 yearsof relevant experience in an AI/ML development role, Applications Development, or Systems Analysis, with a substantial and demonstrated focus on Python technologiesMinimum 2+ yearsof professional experience in software development with a focus on AI, prompt engineering, machine learning, and/or agentic AI systemsProven track record as alead developer for agentic flow design, prompt design, and testingof autonomous AI systems with deep expertise inGoogle ADKSubject Matter Expert (SME)in at least one area of Applications Development, particularly Python application development (Django, Flask, FastAPI)ProgrammingPython (expert‑level): FastAPI, Django, Flask, asyncio, PySpark — strong fundamentals in algorithms, data structures, concurrency, and design patternsProficient in Java (Spring Boot, Spring Cloud), JavaScript/TypeScript (React, Next.js, Node.js), and SQL/data modelingExperience across AWS, Azure, and GCP with Docker, Kubernetes, and CI/CD pipelines. Proficient in MLOps practices including model versioning, deployment, and lifecycle managementStrong foundation in secure API design, microservices, event‑driven architecture, and distributed systems with expertise in testing, Git workflows, and performance optimizationAgentic AI & LLM FrameworksDeep expertise in LLMs (OpenAI GPT, Gemini, Claude, Llama) with hands‑on experience in LangChain, LangGraph, LlamaIndex, AutoGen, CrewAI, and Google ADKFamiliar with Vertex AI, MCPs, agent communication standards, and AI coding tools including GitHub Copilot, Devin, and Claude CodeProven experience building advanced RAG systems (hybrid search, re‑ranking, metadata filtering) with vector databases including Pinecone, Weaviate, FAISS, pgvector, and ChromaDBHands‑on experience in PyTorch, TensorFlow, Keras, and Scikit‑learn including fine‑tuning and embeddingsGood to HavePerformance Optimization : Redis, Hazelcast; low‑latency distributed systemsData Engineering : ETL/ELT pipelines; Apache Spark, KafkaFrontend : React, Angular, Vue.js for full‑stack capabilitiesEducation:Bachelor’s degree/University degree or equivalent experienceMaster’s degree preferredThis job description provides a high‑level review of the types of work performed. Other job‑related duties may be assigned as required.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.#J-18808-Ljbffr