
AI/ML Solutions Engineer
Required skills
- AI/ML
- data engineering
- generative AI
- financial services
- AWS SageMaker
- data pipelines
- ETL
- large language models
- model risk assessment
- model benchmarking
Nice to have
- chatbots
- content generation
- AI assistants
- model validation
- model governance
- quantitative model risk assessment
Role summary
BankUnited is seeking an AI/ML Solutions Engineer with expertise in AI/ML technologies and data engineering to join their data analytics team. The role involves designing, developing, and deploying end-to-end AI/ML solutions for financial services, customer experience enhancement, and operational optimization. Responsibilities include building data pipelines, deploying ML models on AWS SageMaker, developing ETL processes, and evaluating/deploying LLMs. Success requires practical implementation experience across the AI solution lifecycle within a regulated banking environment, focusing on resilience, adaptability, and compliance with model risk management and banking regulations.
- 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.
- 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.
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