Software Engineer II - Backend & Core Systems

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Full Time Regular St. Clair Shores, MI, US

Fisher Dynamics is the automotive industry’s premier supplier of safety – critical seat structures and mechanisms. Steeped in a tradition of excellence, and rooted in automotive innovation, the Fisher story is filled with automotive manufacturing milestones. We bring design, engineering, and manufacturing vehicle seating systems to a new level with innovative thinking. We’re about cutting edge ideas. We have created an environment that encourages an uninterrupted flow of revolutionary concepts and unique ideas.

The SW Engineer - II (Backend & Core Systems) will architect and develop the financial and data backbone of Fisher Dynamics' custom ERP platform from the ground up. They will design and build core ERP modules including General Ledger, Accounts Payable/Receivable, Financial Management, and master database architecture, while being the architect of these systems with AI-native capabilities embedded from the start — financial anomaly detection, fraud prevention, cash flow forecasting, and intelligent account reconciliation powered by LLMs.

This position will work closely with ML/AI engineers to design APIs and data structures that seamlessly integrate intelligent features. They will also ensure the backend is robust, secure, performant, and positioned to support real-time AI-powered financial operations across the enterprise.

Candidates MUST be local to the Metro Detroit Area. Relocation is not available.

• Financial Core Module Development
  • Design and develop General Ledger, Chart of Accounts, and journal entry processing systems with multi-entity support.
  • Build Accounts Payable and Accounts Receivable modules with payment workflows, aging analysis, and financial reconciliation.
  • Implement financial close and consolidation processes with audit trail and compliance requirements.
  • Develop reporting APIs and data structures for financial intelligence and analytics.
• AI-Native Financial Intelligence Architecture
  • Design APIs and data structures that enable seamless embedding of AI/LLM models into financial workflows.
  • Build inference APIs for real-time financial anomaly detection, fraud scoring, and cash flow forecasting.
  • Implement feature export pipelines that feed ML models with financial transaction data and account hierarchies.
  • Design audit logging and compliance structures to support AI-powered financial recommendations and decisions.
• Database Architecture & Core Data Management
  • Design normalized, scalable database schemas for financial, transactional, and master data.
  • Implement data integrity controls, foreign key relationships, and referential integrity for financial accuracy.
  • Optimize queries and indexing for high-volume financial transaction processing.
  • Build data migration pipelines to import legacy financial data from Plex.
  • Implement data governance, retention policies, and compliance controls.
• Develop RESTful and event-driven APIs that expose financial modules to frontends, integrations, and third-party systems.
• Implement secure data access layers with role-based access control (RBAC) for different financial user types.
• Build event streaming for real-time financial data updates and downstream AI model inference.
• Ensure APIs handle complex financial rules, multi-entity operations, and transaction consistency.
• Deploy financial systems on cloud infrastructure (AWS, GCP, Azure) with high availability and disaster recovery.
• Build CI/CD pipelines, containerization (Docker), orchestration (Kubernetes), and infrastructure-as-code.
• Implement monitoring, logging, alerting, and performance optimization for financial systems.
• Ensure compliance with financial security standards and SOX audit requirements.
• Collaboration with AI/ML Engineers
  • Partner with ML/AI engineers to design APIs that connect financial models to ERP transactions.
  • Ensure financial data pipelines support real-time model inference for anomaly detection and forecasting.
  • Support A/B testing and validation of AI-powered…