C

Citi

Runs banking and payments for global clients

Vice President Tech Lead - Multiple Teams

AI Application EngineeringConfirmed live in the last 24 hours
Full-timeHybrid

About the Job

Requirements

  • Deep, hands-on expertise in core generative AI concepts, including foundation models, large language models, embeddings, tokenization, and context-window management.
  • Advanced skills in prompt engineering and context engineering, including familiarity with prompt design tools/frameworks and dynamic context orchestration.
  • Strong experience building retrieval-augmented generation systems, including chunking strategies, hybrid search, and multi-vector retrieval.
  • Practical experience designing knowledge graphs and Graph RAG pipelines, for example using graph databases such as Neo4j or ArangoDB, for relationship-aware, multi-hop retrieval.
  • Proven experience building agentic AI systems with Google Agent Development Kit and/or comparable frameworks (LangGraph, Microsoft Agent Framework, CrewAI, OpenAI Agents SDK), including tool/function calling, planning, and memory.
  • 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: the Model Context Protocol for tool/data access and the Agent2Agent protocol for inter-agent collaboration.
  • Experience with agent observability and evaluation, such as tracing and OpenTelemetry-based tooling, for production agent systems.
  • Proficiency with major generative AI APIs (OpenAI, Gemini, Claude, etc.) and orchestration frameworks such as LangChain and LlamaIndex.
  • Strong skills in natural language processing, including named entity recognition, 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 skills in Python and experience with data preprocessing, document ingestion, and API development.
  • Strong collaboration skills to work effectively in cross-functional teams.
  • Clear communication skills for technical and non-technical audiences.
  • Bachelor's or master's degree in Computer Science, Data Science, AI, or a related field.
  • Five or more years of experience in AI/software development, including significant experience in generative AI and agentic AI.
  • A demonstrated portfolio of successful AI-driven projects in a business environment.
  • Experience working with AWS or equivalent cloud infrastructure for AI/generative AI.

Responsibilities

  • Collaborate with AI architects, leads, and stakeholders to design and implement generative and agentic AI solutions that address business challenges.
  • Architect advanced context engineering strategies, including context layering, chaining, compression, pruning/offloading, and memory management, to maximize reliability, provenance, and token efficiency in production.
  • Design and implement advanced generative AI methods, including sophisticated prompt engineering and retrieval-augmented generation.
  • Build and optimize retrieval-augmented generation systems, including hybrid search, multi-vector retrieval, and re-ranking pipelines.
  • Design and implement knowledge graphs and Graph RAG architectures to enable multi-hop reasoning, explainability, and traceable, grounded responses for high-value business domains.
  • Architect agentic workflows and multi-agent systems using Google Agent Development Kit and comparable frameworks (LangGraph, Microsoft Agent Framework, CrewAI), applying orchestration patterns such as supervisor/worker, hierarchical, and peer-to-peer.
  • Design robust agent harnesses, including governance, constraints, feedback loops, state/session management, and execution controls, to make long-running agent systems reliable and safe.
  • Integrate agents with tools and data via the Model Context Protocol and orchestrate inter-agent collaboration and task delegation via the Agent2Agent protocol.
  • Support the integration of generative AI and agentic applications into production environments, ensuring robust deployment, scalability, observability, and maintainability.
  • Contribute to the development and optimization of real-time and streaming AI solutions.
  • Stay current with advances in generative and agentic AI and share knowledge with the team.
  • Ensure adherence to ethical AI guidelines, guardrails, agent isolation/sandboxing, data privacy, and compliance standards.
  • Mentor junior team members, provide code reviews, and foster a culture of technical excellence.

About the Company

Citi

Runs banking and payments for global clients

Citi runs banking, investment and payment services for companies, governments and individuals around the world. Its global network connects institutions with local banking systems, while its US consumer business sells credit cards and deposit accounts. Corporate clients use Citi to manage cash, finance trade and raise capital, and investors use its trading, custody and wealth services to hold and manage assets. The bank earns interest on loans and fees from payments, securities transactions, financial advice and account services. Citi traces its banking roots to 1812; the Citigroup holding company was formed in 1998, and its headquarters are in New York City.

Company size
10,001+
Company stage
IPO
Headquarters
New York City, New York
Founded
1812

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