Sr Applied AI Engineer-Finance

Dematic is standing up a Finance AI enablement team to drive adoption, build, and roll out AI and sophisticated analytics use cases across the global function.
As the Applied AI / Machine Learning Engineer, you will play a hands‑on role crafting, developing, deploying, and operating AI and ML solutions tailored to Finance use cases such as financial planning, predictive analysis, irregularity identification, and management reporting.
This role is ideal for someone who can build production‑ready models, translate business problems into AI solutions, and operate optimally in an environment that is early in its AI maturity with limited existing technical infrastructure.
The position will partner closely with Finance team members, IT (Dematic & parent co.), and other enterprise AI initiatives to ensure solutions are scalable, auditable, and aligned with standards.We offer:

• Career Development
• Competitive Compensation and Benefits
• Pay Transparency
• Global Opportunities

Learn More Here: https://www.dematic.com/en-us/about/careers/what-we-offer

Dematic provides equal employment opportunities to all employees and applicants for employment and prohibits discrimination and harassment of any type without regard to race, color, religion, age, sex, national origin, disability status, genetics, protected veteran status, sexual orientation, gender identity or expression, or any other characteristic protected by federal, state or local laws.

This policy applies to all terms and conditions of employment, including recruiting, hiring, placement, promotion, termination, layoff, recall, transfer, leaves of absence, compensation and training.

The base pay range for this role is estimated to be $113,625 - $174,225 at the time of posting. Final compensation will be determined by various factors such as work location, education, experience, knowledge, and skills.

Tasks and Qualifications:

What you will do in this role:

AI / ML Solution Development

• 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.

Model Validation & Governance

• 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.

ML Ops & Deployment

• 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.

Collaboration & Enablement

• 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.

What we are looking for:

• 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.

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