AI/ML Solutions Engineer

About the Role

Key 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-tunes, and deploys large language models for financial services applications
• Performs comprehensive model risk assessments and develops mitigation strategies
• Designs and implements model benchmarking frameworks to evaluate performance, bias, and robustness
• Documents model limitations and establishes 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 anti-money laundering laws
• Identifies and reports suspicious activity

Requirements

• Bachelor's Degree in Computer Science, Data Science, Mathematics, or related field or experience
• 5+ years of experience
• Proven track record of deploying generative AI solutions in production environments
• Experience building and deploying machine learning models in production environments
• Experience implementing model validation techniques and performance benchmarking
• Experience with AWS data services (S3, Glue, Redshift, Athena)
• Experience with SQL and NoSQL databases
• Strong programming skills in Python and experience with ML frameworks (PyTorch, TensorFlow, Hugging Face)
• Strong understanding of model risk management frameworks and methodologies
• Knowledge of best practices for mitigating AI-specific risks, including bias, drift, and adversarial vulnerabilities
• Familiarity with cloud-based ML platforms (AWS SageMaker, Azure ML, GCP Vertex AI)
• Familiarity with data pipeline orchestration tools (e.g., AWS Step Functions, Airflow)
• Understanding of data modeling and database design principles
• Understanding of NLP concepts and experience working with language models
• Knowledge of data security and privacy considerations, especially in financial contexts
• Strong collaborative skills and experience working in cross-functional teams
• Ability to communicate complex technical concepts to non-technical stakeholders

Nice to Have

• Experience working with data scientists, product managers, and business units
• Experience developing model governance frameworks compatible with financial service regulations
• Experience with quantitative model risk assessment and establishment of risk thresholds
• Proven track record working with various Machine Learning Models for business use cases
• Expertise in designing controlled testing environments to benchmark model performance
• Experience implementing RAG (Retrieval-Augmented Generation) systems and other LLM-enhancement architectures
• Experience in a regulated industry, particularly financial services
• Experience with vector databases and semantic search technologies
• Experience developing scalable and maintainable AI infrastructures
• Familiarity with AI orchestration and agent technologies
• Knowledge of prompt engineering and model fine-tuning techniques
• Understanding of AI explainability and bias mitigation approaches
• Deep understanding of cloud architecture principles for modular, adaptable systems
• Demonstrated ability to anticipate technological changes and build evolvable solutions

Qualifications

• Bachelor's Degree in Computer Science, Data Science, Mathematics, or related field or experience
• 5+ years of relevant experience

Benefits & Perks

• Opportunities to work on cutting-edge AI/ML projects in the financial industry
• Collaborative work environment
• Professional development and continuous learning opportunities