AI/ML Solutions Engineer

United States·Miami LakesNormalmid

SalesSales Engineer

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Quick Summary

Key Responsibilities

Designs and implements generative AI applications for financial use cases, including document analysis, conversational interfaces,

Technical Tools

SalesSales Engineer

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