Artificial Intelligence Engineer

AI Engineer/Banking Location: East Lansing, MI or Detroit, MI — 5 Days a Week Type of Interview Required: Video Required Qualifications Strong, demonstrable Python development skills (production-level code, not just notebooks/scripting)Prior experience working within a bank or financial services institution — direct exposure to banking data, systems, and/or regulatory environment is requiredExperience with core ML/AI libraries and frameworks (e.g., PyTorch, TensorFlow, scikit-learn, Hugging Face, LangChain, or similar)Solid understanding of data engineering fundamentals — SQL, data pipelines, cloud or on-prem data platformsExperience deploying models to production (MLOps practices, APIs, containerization a plus)Strong communication skills, with the ability to work cross-functionally with both technical and business teamsBachelor's degree in Computer Science, Data Science, Engineering, or related field (or equivalent practical experience)Experience with generative AI/LLM implementations in a regulated industryFamiliarity with cloud platforms (Azure, AWS, or GCP)Exposure to model risk management or model governance frameworks common in bankingExperience with core banking or lending platformsKey Responsibilities Design, develop, and deploy AI/ML models and pipelines to support banking operations, risk, fraud detection, underwriting, customer experience, or process automation use casesWrite clean, production-grade Python code for data processing, model development, and integration with existing systemsCollaborate with data engineering, IT, and business stakeholders to identify opportunities for AI/ML applications within a regulated banking environmentBuild and maintain data pipelines (ETL/ELT) to support model training and inferenceEvaluate, fine-tune, and deploy both traditional ML models and modern LLM/generative AI solutions where applicableEnsure models and AI systems meet banking industry standards for security, data privacy, and regulatory complianceDocument model methodology, performance, and monitoring processes for internal governance and audit purposesPartner with IT infrastructure teams on deployment, scaling, and monitoring of AI solutions in production