Senior Applied AI Engineer – Finance

KION Group

. 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).

Posted 5/12/2026full-timeGrand Rapids • Missouri • 🇺🇸 United StatesSenior💰 $113,625 - $174,225 per yearWebsite

Tech Stack

Tools & technologies

CloudPandasPythonPyTorchTensorflow

About the role

Key responsibilities & impact

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.
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.

Requirements

What you’ll need

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.

Benefits

Comp & perks

Career Development
Competitive Compensation and Benefits
Pay Transparency
Global Opportunities

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills & Tools

machine learningdata scienceAI solutionsML modelsPythonpandasscikit-learnPyTorchTensorFlowMLOps

Soft Skills

strong communication skillsability to explain technical conceptscollaborationproblem-solvingadaptability