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# Application Architect

- **Company:** Genesis10
- **Location:** Plano, TX, United States
- **Experience:** Experienced
- **Salary:** $121,285.0 - $137,925.0
- **Contract:** Temporary contract
- **Skills:** Agile Methodology, Artificial Intelligence, JIRA, Microsoft Azure, Computational Linguistics, DevOps, Interaction Design, Python (Programming Language), Key Management, Machine Learning, Natural Language Processing, Named Entity Recognition, Search Technologies, Subversion, Speech Recognition, Data Classification, Chatbots, Large Language Models, Multi-Agent Systems, Prompt Engineering, Generative AI, Git, Virtual Agents, Natural Language Understanding, Software Version Control
- **Published:** August 21, 2026
- **Apply:** https://www.dice.com/job-detail/1b3daa1d-3b54-4010-aeaa-b4d60f50c221

## About the Role

* 2+ years of experience with conversational interfaces, natural language processing, and conversational AI systems
 * Strong understanding of intent recognition, intent classification, entity extraction, semantic search, and dialogue management concepts
 * Experience training machine learning algorithms for data classification, speech recognition, and natural language understanding
 * Experience developing and evaluating text summarization solutions using Large Language Models (LLMs) and Generative AI technologies
 * Knowledge of Agentic AI frameworks, multi-agent systems, autonomous task execution, tool orchestration, and reasoning-based AI architectures
 * Experience with Python and AI/ML development libraries and frameworks
 * Unique skillset in computational linguistics combined with strong technical and analytical expertise
 * Working knowledge of LLMs, Generative AI, and conversational AI platforms
 * Familiarity with version control and development lifecycle tools such as Git, SVN, JIRA, and Azure DevOps
 * Experience working in DevOps and Agile environments
 * Strong analytical, troubleshooting, and problem-solving skills
 * Strong verbal and written communication skills with the ability to translate linguistic insights into AI model improvements and business value

## Description

This role is responsible for training and maintaining machine learning models for a multi-channel Virtual Assistant. The ideal candidate will leverage Natural Language Understanding (NLU), Large Language Models (LLMs), and Agentic AI capabilities to deliver intelligent, context-aware conversational experiences across web, mobile, and voice channels., * Understand the intent portfolio for NLU across domains (e.g., technology, human resources) and how it maps to conversation design for web, mobile, and voice channels

 * Design, develop, and optimize intent recognition frameworks to improve user request classification, intent resolution, and conversational accuracy across channels
 * Identify and build appropriate datasets to train and test machine learning models for intent classification, entity extraction, speech recognition, and semantic understanding
 * Develop tools and telemetry that can measure and monitor accuracy, performance, intent recognition quality, and model effectiveness throughout the development lifecycle
 * Develop and implement summarization capabilities using LLMs to generate concise, accurate summaries of user interactions, knowledge content, and conversational outcomes
 * Develop a conversational AI strategy leveraging both NLU, LLMs, and Agentic AI architectures to enable autonomous task orchestration, workflow execution, and multi-step problem solving
 * Work with agentic workflows that enable AI agents to reason, plan, retrieve information, invoke tools, and execute tasks while maintaining conversational context and governance requirements
 * Develop disambiguation, clarification, and error-handling strategies as the virtual assistant scales across domains and user populations
 * Monitor conversations and interaction analytics to identify underperforming intents, summarization gaps, and agent behaviors, and develop solutions to improve performance
 * Evaluate and optimize prompt engineering, retrieval-augmented generation (RAG), summarization quality, intent detection accuracy, and agent decision-making performance
 * Collaborate with data scientists, product owners, UX researchers, engineers, and AI specialists to build and continuously improve the "brain" of the virtual assistant


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