VP ML Systems Architect — Global Finance

Citibank (Switzerland) AG; Jersey City (NJ); On-site; Confidential; Full time

Benefits offered by this job

Medical, dental & vision coverage

401(k)

Life, accident & disability insurance

Wellness programs

Paid time off

Job summary

Citigroup’s Citi is seeking a Vice President-level Machine Learning Systems Engineer to architect, deploy, and maintain scalable ML backends that power advanced data science initiatives. You will bridge data science with production-grade services, leading ML pipelines from design to operation across global teams.

The role emphasizes operationalizing complex models, building reliable systems, and driving lifecycle management of DS workflows at enterprise scale.

Qualifications

• 7+ years of experience delivering data-intensive, large-scale ML solutions with leadership experience.
• Proficient in Python, Jupyter, and ML lifecycle management.
• Experience with ML frameworks (TF, PyTorch) and MLOps tooling (MLFlow, Dagster, Vertex AI).

Responsibilities

• Own ML systems architecture and end-to-end delivery of analytics and ML initiatives.
• Lead cross-functional teams and mentor engineers on scalable backend services.
• Ensure production readiness, deployment, and operations of DS models.
• Engage stakeholders to align technical solutions with business objectives.
• Drive quality through robust data pipelines and governance.
• Ensure compliance and risk considerations in ML deployments.

Skills

Python

Jupyter Notebook

ML Ops

TensorFlow

PyTorch

Keras

NLP/LLM

Git

Education

Bachelor’s or Master’s in CS/Engineering

Tools

MLFlow

Dagster

Vertex AI

Jenkins

Kubernetes

Job description

Citigroup’s Citi is seeking a Vice President-level Machine Learning Systems Engineer to architect, deploy, and maintain scalable ML backends that power advanced data science initiatives. You will bridge data science with production-grade services, leading ML pipelines from design to operation across global teams.

The role emphasizes operationalizing complex models, building reliable systems, and driving lifecycle management of DS workflows at enterprise scale.