Sr Applied AI Engineer-Finance

KION•Grand Rapids, MI

About The Position

Dematic is establishing a Finance AI enablement team to drive the adoption, development, and rollout of AI and advanced analytics use cases across the global function. As the Applied AI / Machine Learning Engineer, you will be instrumental in designing, developing, deploying, and operating AI and ML solutions specifically for Finance use cases. These include financial planning, predictive analysis, irregularity identification, and management reporting. This role is suited for an individual who can create production-ready models, translate business challenges into AI solutions, and thrive in an environment with developing AI maturity and limited existing technical infrastructure. You will collaborate closely with Finance team members, IT (Dematic & parent company), and other enterprise AI initiatives to ensure solutions are scalable, auditable, and adhere to established standards.

Requirements

Bachelor’s degree in Computer Science, Data Science, Engineering, Applied Mathematics, Finance, or a related field.
4-7+ years of proven experience in machine learning, data science, or applied AI with hands-on production deployment experience.
Strong experience building ML models using Python and common libraries (e.g., pandas, scikit-learn, PyTorch, TensorFlow).
Experience developing or integrating LLM-based solutions (prompt engineering, embeddings, retrieval-augmented generation, summarization).
Proven understanding of time-series forecasting, anomaly detection, regression, and classification techniques.
Experience with model validation, back-testing, performance monitoring, and explain-ability.
Practical experience implementing MLOps concepts (CI/CD for models, monitoring, version control).
Ability to work in a low-maturity AI environment, creating structure where little exists.
Strong communication skills with the ability to explain technical concepts to Finance and business audiences.

Responsibilities

Design, develop, and deploy machine learning models and AI solutions tailored to Finance use cases (e.g., forecasting, planning, variance analysis, anomaly and risk detection).
Build and maintain ML models for financial planning, forecasting, trend analysis, and anomaly detection across large, structured datasets.
Develop LLM-powered tools to support financial analysis, commentary generation, summarization, and scripted insights for Finance users.
Translate Finance requirements into data pipelines, feature engineering, model architecture, and deployment approaches.
Conduct model validation, back-testing, and performance evaluation to ensure accuracy, robustness, and business relevance.
Evaluate model performance over time and diagnose issues related to data quality, concept drift, and changing business conditions.
Implement appropriate controls, explain-ability, and documentation to support Finance governance, audit, and compliance requirements.
Document model assumptions, methodologies, limitations, and change history for audit and risk review.
Implement MLOps standard methodologies, including model versioning and lifecycle management, drift detection and performance monitoring, retraining schedules and automated pipelines, and reproducibility and rollback procedures.
Partner with IT to deploy models into enterprise environments (cloud, Salesforce, SAP, Snowflake, proprietary tools, etc.).
Ensure AI solutions are secure, scalable, and maintainable within enterprise standards.
Collaborate with Finance, IT, data teams, and other AI workstreams to promote consistent standards, tooling, and patterns across the organization.
Serve as a technical thought partner to Finance leaders, helping shape the AI roadmap and identify high-value use cases.
Help educate Finance partners on AI capabilities, limitations, and responsible usage.
Contribute to establishing foundational AI practices for a growing Finance AI team.