Principal AI Engineer Vice President

Citi

Confirmed live yesterday High trust

Remote

Quick summary

Work type: Remote
Location: Tampa, FL
Salary: $125,600–$188,400 / yr
Posted: 85 days ago
Freshness: Confirmed live yesterday
Nearby: 99+ roles within 25 mi

Market check

Salary context

Below market

How this pay compares to similar roles

Similar $207k

This role $157k

$109k most similar roles pay here $279k

This role pays less than 86% of similar roles. Most pay $171,387–$242,162 — the shaded band above. At the midpoint, this role pays about $157k versus about $207k for comparable roles.

Based on 240 similar postings.

Employer

About Citi

Citi is one of the world’s most trusted financial institutions, proudly serving millions of customers across the United States.

Citi currently has 256 open roles on FindRole.

Listed pay typically runs $140,080–$210,120 across 238 roles with salary data.

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View all roles at Citi

At a glance

TL;DR · Principal AI Engineer Vice President

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Principal AI Engineer - Vice President The Principal AI Engineer - Vice President serves as a strategic technical leader within the Digital Software Engineering team, designing and engineering enterprise-grade Agentic AI solutions. This role involves building autonomous agents, multi-agent orchestration, and robust data pipelines to integrate information from heterogeneous systems for automated decisioning. You will architect fault-tolerant systems using frameworks like LangGraph, LangChain, CrewAI, and Google_ADK while managing RAG architectures and advanced prompt engineering. The technical stack includes Python, PyTorch, TensorFlow, FastAPI, and various vector databases such as Pinecone or Milvus. Additionally, you will deploy containerized applications using Kubernetes and Docker within MLOps pipelines. You will solve complex problems involving large-scale unstructured data processing and ensure secure, compliant AI deployments while mentoring engineers on best practices for LLM integration and high-quality engineering standards.

Skills

LLMs RAG Python LangChain LlamaIndex LangGraph Google_ADK Kubernetes Docker Pinecone PyTorch TensorFlow FastAPI CI/CD MLOps OpenAI Gemini Claude Mistral Llama HuggingFace Neo4j MongoDB Atlas

What you'll do

Design and build agentic AI systems featuring multi-agent orchestration, tool use, and adaptive decision-making workflows.
Architect fault-tolerant, scalable AI solutions using frameworks like LangGraph, LangChain, or Google_ADK.
Develop advanced Retrieval-Augmented Generation (RAG) architectures including hybrid retrieval and query planning.
Build robust data pipelines to ingest and harmonize information from diverse sources for LLM workloads.
Engineer prompt templates, system instructions, and safety guardrails to ensure deterministic AI behavior.
Deploy production-grade GenAI applications using containerized infrastructure and MLOps/AIOps CI/CD pipelines.
Serve as a technical subject matter expert to mentor engineers and influence product strategy.

What we're looking for

10+ years of experience in software engineering, AI/ML engineering, systems architecture, or related fields.
Proven experience designing and deploying enterprise-grade AI systems in production environments.
Strong foundations in ML, NLP, embeddings, statistics, neural networks, and LLMs.
Deep expertise in RAG architectures, vector search, and semantic data modeling.
Experience building agentic AI using frameworks such as Google_ADK or LangGraph.
Proficiency in Python and libraries including Pandas, NumPy, PyTorch, TensorFlow, FastAPI, LangChain, and LlamaIndex.
Experience with vector databases like Pinecone, PGVector, MongoDB Atlas Vector Search, Neo4j, or Milvus.
Expertise in deploying GenAI solutions using Kubernetes, Docker, Helm, and CI/CD pipelines.
Bachelor’s degree required; Master’s degree preferred.

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