Senior VP, Enterprise AI Platform & Automation

Citigroup; Tampa (FL); On-site; USD 150,000 - 230,000; Full time

Job summary

Citigroup seeks a Digital Software Engineering Lead Analyst to design and implement enterprise-grade Agentic AI solutions, integrating data across systems and scaling with fault tolerance. You will drive AI system blueprints, data pipelines, and RAG architectures while collaborating with Data Engineering, Enterprise Platforms, and Product teams.

You will lead hands-on design, embedding best practices in ML/AI, deployment, and governance, ensuring secure and compliant AI workloads in a dynamic

Qualifications

• 10+ years in software/AI engineering or architecture.
• Proven enterprise AI systems deployments in production.
• Strong ML/LLM foundations and RAG experience.
• Hands-on with Python and AI tooling.

Responsibilities

• Design and build agentic AI systems with multi-agent orchestration.
• Architect fault-tolerant, scalable AI solutions and end-to-end blueprints.
• Lead data integration, pipelines, and knowledge orchestration for LLM workloads.
• Develop GenAI apps with containerized cloud infrastructure and MLOps pipelines.
• Mentor engineers and influence platform strategy on GenAI trends.

Skills

ML/AI engineering

LLMs

RAG architectures

Python

Data pipelines

Cloud engineering

MLOps

Agentic systems

Education

Bachelor's degree in CS/related field

Tools

LangChain

LangGraph

Google_ADK

OpenAI

Pinecone

PGVector

MongoDB Atlas Vector Search

Neo4j

Milvus

Kubernetes

Docker

Helm

GitHub Actions

Jenkins

Azure DevOps

ArgoCD

OpenShift

Job description

Citigroup seeks a Digital Software Engineering Lead Analyst to design and implement enterprise-grade Agentic AI solutions, integrating data across systems and scaling with fault tolerance. You will drive AI system blueprints, data pipelines, and RAG architectures while collaborating with Data Engineering, Enterprise Platforms, and Product teams.

You will lead hands-on design, embedding best practices in ML/AI, deployment, and governance, ensuring secure and compliant AI workloads in a dynamic