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# Application Architect

- **Company:** Genesis10
- **Location:** Plano, TX, United States
- **Salary:** $121,285.0 - $137,925.0
- **Contract:** Temporary contract
- **Skills:** Microsoft Excel, Software Design Patterns, Machine Learning, Microsoft Office, Performance Tuning, Speech Recognition, Model Validation, Machine Learning Operations, Natural Language Understanding
- **Published:** August 11, 2026
- **Apply:** https://www.dice.com/job-detail/27c35f09-7097-49f2-8c69-6bb9d98d43d4

## About the Role

* Proven experience architecting and maintaining NLU intent frameworks
 * Experience designing and curating datasets for machine learning pipelines
 * Ability to develop model evaluation frameworks and telemetry pipelines
 * Knowledge of designing scalable error-handling architectures for virtual assistants
 * Experience with Microsoft Office, including Excel

## Description

The successful candidate will be responsible for architecting, developing, and maintaining machine learning models and NLU frameworks that identify end-user intent across a multi-channel Virtual Assistant platform. This role focuses on designing scalable conversational AI solutions, optimizing model performance, and ensuring the underlying intent recognition architecture effectively supports web, mobile, and voice-based experiences., * Architect and maintain the NLU intent framework across enterprise domains (e.g., Technology, Human Resources), ensuring alignment with conversational solution design patterns for web, mobile, and voice experiences

 * Design, engineer, and curate training and validation datasets to support machine learning pipelines for intent classification, speech recognition, and conversational AI performance optimization
 * Develop and maintain model evaluation frameworks, telemetry pipelines, and observability tools to measure accuracy, reliability, and operational performance, enabling data-driven model tuning throughout the development lifecycle
 * Design scalable disambiguation, fallback, and error-handling architectures that improve resilience and user experience as virtual assistant capabilities expand across domains and channels
 * Analyze conversational telemetry, interaction flows, and runtime performance metrics to identify gaps, root causes, and optimization opportunities, implementing architectural improvements that enhance system effectiveness
 * Partner with data scientists, product owners, UX researchers, and software engineers to design, build, and evolve the cognitive architecture and decisioning framework that powers the virtual assistant ecosystem


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