Banking AI/ML Solutions Architect

bankunitedexternal; Town of Florida (NY); Hybrid; USD 140,000 - 190,000; Full time

Job summary

BankUnited is seeking an AI/ML Solutions Engineer to design, develop, and deploy end-to-end AI/ML solutions for financial services, customer experience, and operations. The role emphasizes data engineering on AWS and collaboration across cross-functional teams within a regulated banking environment.

The ideal candidate has strong Python skills, experience with ML frameworks, and a proven production track record of deploying AI systems, including LLMs and retrieval-augmented generation.

Qualifications

• Bachelor's or higher in CS/DS/Math or related field.
• 5+ years of experience deploying generative AI solutions in production.
• Experience deploying ML models in production environments.
• Experience with AWS data services (S3, Glue, Redshift, Athena).
• Experience with SQL and NoSQL databases.
• Experience collaborating with data scientists, product managers, and business units.

Responsibilities

• Design and implement generative AI applications for financial use cases.
• Develop and maintain ML pipelines using AWS SageMaker or similar platforms.
• Build data pipelines to support AI/ML workflows on AWS.
• Develop ETL processes to prepare data for models.
• Collaborate with stakeholders to identify AI opportunities and requirements.
• Evaluate, fine-tune, and deploy large language models for financial services.

Skills

Python

ML frameworks

AWS SageMaker

Data pipelines

NLP

Model risk

Cloud architecture

Team collaboration

Explainability

Education

Bachelor's Degree in CS/DS/Math

Tools

Airflow

SQL

NoSQL

Vector databases

Job description

BankUnited is seeking an AI/ML Solutions Engineer to design, develop, and deploy end-to-end AI/ML solutions for financial services, customer experience, and operations. The role emphasizes data engineering on AWS and collaboration across cross-functional teams within a regulated banking environment.

The ideal candidate has strong Python skills, experience with ML frameworks, and a proven production track record of deploying AI systems, including LLMs and retrieval-augmented generation.