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# AI/ML ARCHITECT - ENTERPRISE FINANCE

- **Company:** Xpertiz INC
- **Location:** Minneapolis, MN, United States
- **Experience:** Starter
- **Contract:** Contract
- **Skills:** Application Programming Interfaces (APIs), Artificial Intelligence, Cloud Engineering, Information Engineering, Monitoring of Systems, Information Retrieval, Machine Learning, Search Technologies, Software Deployment, Software Engineering, Retrieval-Augmented Generation, Large Language Models, Prompt Engineering, IT Architecture, Model Validation, Generative AI, Agentic-AI, Build Management, Data Management, Machine Learning Operations
- **Published:** November 4, 2026
- **Apply:** https://www.dice.com/job-detail/86423d68-c898-45b3-a30c-fced3500b9f7

## About the Role

* Strong experience in AI/ML Engineering, Software Engineering, or AI Architecture.
 * Hands-on experience building and deploying production-grade Generative AI and LLM applications.
 * Strong expertise in:
 + Large Language Models (LLMs)
 + Generative AI
 + Retrieval-Augmented Generation (RAG)
 + Agentic AI / AI Agents
 + Prompt Engineering
 + Model Evaluation & Optimization
 + Vector Databases & Semantic Search
 * Strong understanding of cloud-native architectures, APIs, data platforms, and scalable systems.
 * Knowledge of Responsible AI, security, privacy, and compliance practices., * Experience in Enterprise Finance, FP&A, Accounting, Treasury, Tax, Banking, or Financial Services.
 * 1-2+ years of hands-on Agentic AI development and deployment.
 * Experience with AI observability, model monitoring, and knowledge retrieval systems.
 * Experience leading enterprise AI products from design through production support.

## Description

We are looking for an experienced AI/ML Architect / Lead AI/ML Engineer to architect, build, and operationalize AI-powered solutions that drive innovation, productivity, and smarter decision-making across enterprise Finance organizations., * Design scalable AI solution architectures aligned with business objectives and governance requirements.

 * Build and deploy Generative AI, LLM, RAG, and Agentic AI applications.
 * Develop prompt engineering strategies, orchestration workflows, tool-calling patterns, guardrails, and fallback mechanisms.
 * Build intelligent agents, classification models, summarization workflows, and decision-support solutions.
 * Establish model evaluation frameworks, benchmarks, and performance metrics.
 * Monitor and optimize AI output quality, accuracy, latency, cost, and hallucination risks.
 * Implement AI observability, monitoring, and continuous improvement practices.
 * Collaborate with Finance, Data Engineering, Security, Compliance, and Responsible AI teams.
 * Drive AI solutions from architecture and development through production deployment and support.