Senior Agentic AI Engineer - Vice President

Citi; New York (NY); Hybrid; USD 62,000 - 125,000; Full time

Job summary

Citi seeks a Senior Agentic AI Engineer to drive design, development, and integration of state-of-the-art Generative AI and agentic AI solutions across our enterprise Controls Technology platform.

You will collaborate with cross-functional teams on context engineering, RAG, knowledge graphs, Graph RAG, and multi-agent orchestration using ADK, MCP, and A2A protocols. Mentor peers and ensure safe, compliant production deployment.

Qualifications

• Deep expertise in generative AI concepts and foundation models.
• Experience with retrieval and graph-based AI pipelines.
• Proven track record building agentic AI systems and multi-agent orchestration.

Responsibilities

• Collaborate to design generative and agentic AI solutions.
• Architect context engineering strategies for reliability and efficiency.
• Design and implement RAG systems and graph-based retrieval pipelines.
• Build multi-agent systems with orchestration patterns and governance.
• Ensure production readiness with observability and compliance.

Skills

Foundation models

LLMs

RAG systems

Knowledge graphs

Agent frameworks

Multi-agent orchestration

MCP protocol

A2A protocol

Python

Docker

Kubernetes

OpenTelemetry

LangChain

LlamaIndex

NLP

Education

Bachelor's or Master's in CS/AI

Tools

Google ADK

LangGraph

CrewAI

Neo4j

ArangoDB

OpenAI Agents SDK

LangChain

LlamaIndex

Job description

Senior Agentic AI Engineer - Vice President

Job Req Id:

26993138

Location(s):

Pune, Maharashtra, India, Chennai, Tamil Nadu, India

Job Type:

Hybrid

Posted:

Sep. 16, 2026

Discover your future at Citi

Working at Citi is far more than just a job. A career with us means joining a team of approximately 219,000 dedicated people from around the globe. At Citi, you’ll have the opportunity to grow your career, give back to your community and make a real impact.

Job Overview

We are seeking an experienced Senior Agentic AI Engineer to help drive the design, development, and integration of state-of-the-art Generative AI and agentic AI solutions across our enterprise Controls Technology platform. You will collaborate with cross-functional teams, contribute deep technical expertise in context engineering, retrieval systems, knowledge graphs, and multi-agent orchestration, and play a key role in delivering scalable, grounded AI solutions to enhance automation and operational efficiency. This role centers on architecting robust applications and agent systems on top of pre-trained and hosted foundation models — not on training or fine‑tuning models.

Key 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 — 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 (RAG).

• Build and optimize RAG 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 (ADK) and comparable frameworks (LangGraph, Microsoft Agent Framework, CrewAI), applying orchestration patterns such as supervisor/worker, hierarchical, and peer-to-peer.

• Design robust agent harnesses — governance, constraints, feedback loops, state/session management, and execution controls that make long-running agent systems reliable and safe.

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

• Support the integration of GenAI 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 the latest advances in generative and agentic AI and actively 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.

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 familiarity with prompt design tools/frameworks and dynamic context orchestration.

• Strong experience building RAG systems, including chunking strategies, hybrid search, and multi-vector retrieval.

• Practical experience designing knowledge graphs and Graph RAG pipelines (e.g., using graph databases such as 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.

• 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 (MCP) for tool/data access and the Agent2Agent (A2A) protocol for inter-agent collaboration.

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

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

• Strong skills in NLP (NER, dependency parsing, text classification, 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.

Required Soft Skills

• Strong collaboration skills to work effectively in cross-functional teams.

• Analytical and proactive approach to problem-solving.

• Clear communication skills for both technical and non-technical audiences.

• Eagerness to learn, innovate, and mentor less experienced developers.

Qualifications

• Bachelor's or master's degree in Computer Science, Data Science, AI, or a related field.

• Total experience-12+ yrs. 5–7 years of experience in AI/software development, including significant experience in Generative AI and agentic AI.

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

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

Job Family Group:

Technology

Job Family:

Applications Development

Time Type:

Full time

Most Relevant Skills

Please see the requirements listed above.

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Other Relevant Skills

For complementary skills, please see above and/or contact the recruiter.

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