Cognitive Linguist - Onsite

USDC•Charlotte, NC

8h•$58 - $66•Onsite

About The Position

Genesis10 is seeking a Cognitive Linguist for an onsite contract opportunity with a Global Financial Institution. The role involves training and maintaining machine learning models for a multi-channel Virtual Assistant, utilizing Natural Language Understanding (NLU), Large Language Models (LLMs), and Agentic AI to create intelligent, context-aware conversational experiences across web, mobile, and voice channels.

Requirements

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.

Responsibilities

Understand the intent portfolio for NLU across domains and its mapping to conversation design for various channels.
Design, develop, and optimize intent recognition frameworks to improve user request classification, intent resolution, and conversational accuracy.
Identify and build appropriate datasets for training and testing machine learning models for intent classification, entity extraction, speech recognition, and semantic understanding.
Develop tools and telemetry to measure and monitor accuracy, performance, intent recognition quality, and model effectiveness.
Develop summarization capabilities using LLMs to generate concise summaries of user interactions, knowledge content, and conversational outcomes.
Develop a conversational AI strategy leveraging NLU, LLMs, and Agentic AI architectures for 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.
Develop disambiguation, clarification, and error-handling strategies as the virtual assistant scales.
Monitor conversations and interaction analytics to identify underperforming intents, summarization gaps, and agent behaviors, and develop solutions.
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 improve the virtual assistant.