Junior AI/ML Solutions Engineer

Summary

BankUnited is seeking an early-career Junior AI/ML Solutions Engineer to help design, implement, and support end-to-end AI solutions that enhance our financial services, improve customer experiences, and optimize internal operations. In this role, you'll contribute to data pipelines, ML workflows, and generative AI applications under the guidance of senior engineers. You will develop hands-on skills across the AI solution lifecycle from data preparation and infrastructure design to model development and deployment within a regulated banking environment, as well as leveraging Microsoft Copilot agents and Microsoft 365 AI capabilities to optimize internal operations and elevate user productivity.

Essential Duties and Responsibilities

• Contributes features to generative AI applications for document analysis, conversational assistants, and predictive use cases; start with proofs of concept and iterate into production with senior support.
• Helps evaluate, fine-tune, and integrate large language models (LLMs) into workflows; build prompt templates and lightweight evaluation harnesses.
• Assists in developing and maintaining ML pipelines on AWS (e.g., SageMaker), focusing on reproducibility and automation.
• Helps implement data pipelines/ETL to prepare datasets for training and inference using S3/Glue/Athena/Redshift.
• Supports and maintains existing AI/ML solutions, troubleshooting issues and implementing enhancements as needed.
• Supports existing AI/ML solutions through maintenance, issue resolution, and documentation of system behavior and updates.
• Supports model benchmarking and experiment tracking to assess performance, robustness, and bias; document limitations and known failure modes.
• Partners with senior engineers and compliance with model risk management expectations and ongoing monitoring practices.
• Participates in deployments with CI/CD and MLOps practices; assist with alerting, logging, and early risk detection dashboards.
• Leverages Microsoft Copilot Studio and Microsoft 365 Copilot features to build lightweight automation, copilots, prompts, and AI-driven workflows.
• Assists in developing copilots/agents that integrate SharePoint, Teams, Outlook, and Microsoft Graph data sources.
• Collaborates with stakeholders to identify practical AI opportunities; help convert requirements into technical tasks and clear documentation.
• Stays current with advancements in AI/ML.
• Adheres to and complies with applicable, federal and state laws, regulations and guidance, including those related to anti-money laundering (i.e., Bank Secrecy Act, US PATRIOT Act, etc.).
• Adheres to Bank policies and procedures and completes required training.
• Identifies and reports suspicious activity.

Education

• Bachelor's Degree in a related field or equivalent experience required.

Experience

• Up to 1.5 years of experience through coursework or internships with ML or generative AI required.
• Experience (academic or professional) implementing RAG or vector search; familiarity with AI orchestration/agent patterns preferred.
• Experience with explainability and bias mitigation techniques preferred.
• Experience integrating Microsoft Graph APIs or automation across Teams and SharePoint preferred.
• Experience in a regulated industry (especially financial services) preferred.

Knowledge, Skills And Abilities

• Foundational Python skills and exposure to ML frameworks required.
• Familiarity with cloud ML platforms required.
• Basic understanding of Microsoft 365 ecosystem tools (SharePoint, Teams, Outlook) and Copilot capabilities required.
• Basic SQL and data modeling knowledge required.
• Understanding of NLP and LLM concepts required.
• Awareness of data security and privacy considerations required.
• Knowledge of model governance frameworks in financial services and quantitative approaches to risk thresholds preferred.
• Exposure to designing benchmarking or controlled testing environments for model comparisons preferred.
• Understanding of vector databases and semantic search technologies preferred.
• Familiarity with cloud architecture principles that favor modular, interchangeable components preferred.
• Interest in prompt engineering and LLM fine-tuning preferred.

Key skills/competency

• AI/ML Solutions
• Generative AI
• Large Language Models (LLMs)
• AWS ML Platforms
• Python Programming
• Data Pipelines (ETL)
• MLOps & CI/CD
• Microsoft Copilot
• SQL & Data Modeling
• Natural Language Processing (NLP)