VP‑Level ML Systems Architect

Citi; Jersey City (NJ); On-site; USD 142,000 - 213,000; Full time

Job summary

Citi is hiring a Vice President–level Machine Learning Systems Engineer to lead scalable backend ML infrastructure in Jersey City. You will own deployment of data science models, design robust pipelines, and bridge data science with production-ready systems across enterprise applications.

The role emphasizes architecture leadership, end-to-end ML delivery, and collaboration with cross-functional teams to operationalize complex models at scale.

Qualifications

• 7+ years of experience building data-intensive ML solutions.
• Strong architecture and leadership experience in ML systems.
• Proficient in Python and data science tooling.

Responsibilities

• Architect ML systems and pipelines aligned to business goals.
• Lead end-to-end ML delivery from data gathering to deployment.
• Collaborate with data scientists, engineers, and business leaders.
• Drive governance, security, and reliability across ML workloads.
• Mentor teams and influence global projects.
• Oversee lifecycle of DS workflows in enterprise-scale systems.

Skills

ML Systems Architecture

Python

Jupyter Notebook Scripting

MLOps

NLP & Generative AI

Scikit-learn

Keras

TensorFlow

PyTorch

RAGAs

DsPy

LLMs

MLFlow

Dagster

Vertex AI

Kafka

CI/CD

Docker

OpenShift

Amazon ECS

Oracle

MySQL

PostgreSQL

MongoDB

NGINX

FastAPI

Flask

Education

Bachelor’s or Master’s degree in CS/Engineering

Tools

MLFlow

Dagster

Vertex AI

Google Cloud

Docker

OpenShift

Amazon ECS

Oracle

MySQL

PostgreSQL

MongoDB

NGINX

FastAPI

Flask

Job description

Citi is hiring a Vice President–level Machine Learning Systems Engineer to lead scalable backend ML infrastructure in Jersey City. You will own deployment of data science models, design robust pipelines, and bridge data science with production-ready systems across enterprise applications.

The role emphasizes architecture leadership, end-to-end ML delivery, and collaboration with cross-functional teams to operationalize complex models at scale.