The Role
Designs, develops, and deploys end-to-end AI/ML solutions for financial services, including generative AI applications, LLMs, RAG systems, data pipelines, and production machine learning models. Builds AWS-based infrastructure and ETL workflows, performs model validation and risk assessments, establishes benchmarking and monitoring frameworks, and ensures compliance with banking regulations, privacy requirements, and ethical standards. Collaborates with technical and business stakeholders to identify AI opportunities and communicate complex concepts.
Summary Generated by Built In
Job Summary & Responsibilities
Experience
Licenses and Certifications
Knowledge, Skills, and Abilities
Additional Information
JOB SUMMARY: We are seeking an Artificial Intelligence/Machine Leaning (AI/ML) Solutions Engineer specialized in both AI & ML technologies and data engineering to join our data analytics team at BankUnited. In this role, you will design, develop, and deploy end-to-end AI & ML solutions that enhance our financial services, improve customer experiences, and optimize internal operations.This position offers an exciting opportunity to leverage your expertise in AI technologies and data engineering to transform our banking operations. You will be responsible for building comprehensive solutions from data pipeline development to AI/ML model deployment, addressing complex financial services challenges while working in a collaborative environment with cross-functional teams.As an ideal candidate, you will combine technical expertise in AI/ML with strong data engineering skills on AWS to build resilient, adaptable systems that can evolve with rapidly changing technologies. Success in this role requires practical implementation experience across the entire AI solution lifecycle - from data preparation and infrastructure design to model development and deployment - all within a regulated banking environment.
ESSENTIAL DUTIES AND RESPONSIBILITIES:
- Designs and implements generative AI applications for financial use cases, including document analysis, conversational interfaces, and predictive models
- Develops and maintains machine learning pipelines using AWS SageMaker or similar cloud platforms
- Designs and implements data pipelines to support AI/ML workflows on AWS
- Develops ETL processes to prepare data for machine learning models
- Collaborates with stakeholders to identify opportunities for AI implementation and innovation
- Evaluates, fine-tune, and deploys large language models for financial services applications
- Performs comprehensive model risk assessments and develop mitigation strategies
- Designs and implements model benchmarking frameworks to evaluate performance, bias, and robustness
- Documents model limitations and establish monitoring systems for early risk detection
- Works with compliance teams to ensure AI systems meet regulatory requirements for model risk management
- Ensures AI systems comply with banking regulations and ethical standards
- Stays current with rapid advancements in AI technologies and methodologies.
- 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.
QUALIFICATIONS
Education
- Bachelor's Degree in Computer Science, Data Science, Mathematics, or related field or experience required Or
Experience
- 5+ years of experience required
- Proven track record of deploying generative AI solutions in production environments (eg, chatbots, content generation systems, or AI assistants) required
- Experience building and deploying machine learning models in production environments required
- Experience implementing model validation techniques and performance benchmarking required
- Experience with AWS data services (S3, Glue, Redshift, Athena) required
- Experience with SQL and NoSQL databases required
- Experience working with data scientists, product managers, and business units preferred
- Experience developing model governance frameworks compatible with financial service regulations preferred
- Experience with quantitative model risk assessment and establishment of risk thresholds preferred
- Proven track record working with various Machine Learning Models, implementing them for various business use cases preferred
- Expertise in designing controlled testing environments to benchmark model performance against established standards preferred
- Experience implementing RAG (Retrieval-Augmented Generation) systems and other LLM-enhancement architectures preferred
- Experience in a regulated industry, particularly financial services preferred
- Experience with vector databases and semantic search technologies preferred
- Experience developing scalable and maintainable AI infrastructures that accommodate rapid technological advancements
Licenses and Certifications
Knowledge, Skills, and Abilities
- Strong programming skills in Python and experience with ML frameworks (PyTorch, TensorFlow, Hugging Face) required
- Strong understanding of model risk management frameworks and methodologies required
- Knowledge of best practices for mitigating AI-specific risks, including bias, drift, and adversarial vulnerabilities required
- Familiarity with cloud-based ML platforms (AWS SageMaker, Azure ML, or GCP Vertex AI) required
- Familiarity with data pipeline orchestration tools (e.g., AWS Step Functions, Airflow) required
- Understanding of data modeling and database design principles required
- Understanding of NLP concepts and experience working with language models required
- Knowledge of data security and privacy considerations, especially in financial contexts required
- Strong collaborative skills and experience working in cross-functional teams required
- Ability to communicate complex technical concepts to non-technical stakeholders required
- Proficiency in using Amazon Q Business for enterprise operations and solutions required
- Demonstrated passion for AI advancement, with a track record of self-directed learning, experimentation with emerging technologies, and willingness to pioneer innovative approaches preferred
- Familiarity with AI orchestration and agent technologies preferred
- Knowledge of prompt engineering and model fine-tuning techniques preferred
- Understanding of AI explainability and bias mitigation approaches preferred
- Forward-thinking approach to AI system design, with focus on adaptability and resilience preferred
- Deep understanding of cloud architecture principles to design modular, interchangeable systems preferred
- Demonstrated ability to anticipate technological changes and build solutions that can evolve preferred
Additional Information
Candidates residing in locations within BankUnited's footprint may be given preference.
Skills Required
- Bachelor’s degree in Computer Science, Data Science, Mathematics, or a related field, or equivalent experience
- 5+ years of professional experience
- Production deployment experience with generative AI solutions such as chatbots, content generation systems, or AI assistants
- Experience building and deploying machine learning models in production
- Experience implementing model validation techniques and performance benchmarking
- Experience with AWS data services including S3, Glue, Redshift, and Athena
- Experience with SQL and NoSQL databases
- Strong programming skills in Python and experience with PyTorch, TensorFlow, and Hugging Face
- Strong understanding of model risk management frameworks and methodologies
- Knowledge of mitigating AI risks including bias, drift, and adversarial vulnerabilities
- Familiarity with cloud-based ML platforms such as AWS SageMaker, Azure ML, or GCP Vertex AI
- Familiarity with data pipeline orchestration tools such as AWS Step Functions or Airflow
- Understanding of data modeling and database design principles
- Understanding of NLP concepts and experience with language models
- Knowledge of data security and privacy considerations in financial contexts
- Strong collaboration and cross-functional communication skills
- Ability to communicate complex technical concepts to non-technical stakeholders
- Proficiency using Amazon Q Business for enterprise operations and solutions
- Experience working with data scientists, product managers, and business units
- Experience developing model governance frameworks compatible with financial services regulations
- Experience with quantitative model risk assessment and risk thresholds
- Experience implementing machine learning models for multiple business use cases
- Experience designing controlled testing environments for model benchmarking
- Experience implementing RAG systems and LLM enhancement architectures
- Experience in a regulated industry, particularly financial services
- Experience with vector databases and semantic search technologies
- Experience developing scalable, maintainable AI infrastructure
- Familiarity with AI orchestration and agent technologies
- Knowledge of prompt engineering and model fine-tuning
- Understanding of AI explainability and bias mitigation
- Deep understanding of cloud architecture principles
BankUnited Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about BankUnited and has not been reviewed or approved by BankUnited.
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Healthcare Strength — Healthcare coverage is positioned as comprehensive, with medical, dental, and vision options plus disability and life insurance. Wellness programming is described as robust, including incentives, screenings, and on-site fitness facilities at the corporate center.
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Retirement Support — Retirement support includes a 401(k) plan with a company match and relatively quick eligibility after one month. Auto-enrollment and auto-increase features are described, which can help employees build savings consistently.
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Leave & Time Off Breadth — Time-off offerings are described as broad, including a sizable PTO range by level and paid holidays. Additional time-off programs such as volunteer time and flexible/hybrid/remote arrangements are also described for eligible positions.
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The Company
What We Do
BankUnited, Inc., with total consolidated assets of $35.2 billion at March 31, 2021, is a bank holding company with one wholly owned subsidiary, BankUnited. BankUnited, a national banking association headquartered in Miami Lakes, Florida, provides a full range of banking services to individual and corporate customers through banking centers in Florida and New York. The Bank also provides certain commercial lending and deposit products on a national platform. Here at BankUnited, we endeavor to provide, through experienced lending and relationship banking teams, personalized customer service and offer a full range of traditional banking products and services to both commercial and retail customers.
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