Job Description NLP Architect – Generative AI & Conversational Intelligence Location: Durham, NC (Remote) Duration: 12 Months Contract About the Role: We are seeking a Principal AI Architect – Generative AI & NLP to lead the design and deployment of next-generation AI platforms powering intelligent customer experiences. This role will drive innovation across LLM-based conversational AI, agent assist systems, and autonomous CX workflows, enabling scalable, secure, and human-like interactions across global enterprises. You will play a critical role in shaping the future of AI-driven contact center platforms, combining Generative AI, GraphRAG, RLHF, and multi-agent systems to deliver highly personalized, context-aware, and trustworthy customer interactions. Required Qualifications: • 15 years of experience in AI/ML, NLP, or distributed systems • 5 years working with Generative AI and LLM-based systems • Proven experience building production-grade AI platforms at scale • Deep expertise in: 1. GraphRAG architectures (not just RAG) 2. RLHF and alignment systemsn 3. Multi-agent AI systems 4. Distributed training and inference • Strong programming skills in Python, Scala, or Java • Experience with PyTorch, TensorFlow,Transformers Key Responsibilities: AI Architecture & Strategy • Define and lead the architecture of enterprise-scale LLM-driven conversational AI platforms • Design advanced RAG and GraphRAG-based knowledge systems for customer support and agent assist • Architect low-latency, high-throughput inference systems for real-time interactions Generative AI & NLP Systems • Build and optimize LLM pipelines, including fine-tuning (LoRA, QLoRA) and prompt orchestration • Develop multi-agent AI systems for autonomous customer workflows and decision intelligence • Implement semantic search, hybrid retrieval, and contextual reasoning systems • Design multimodal AI capabilities (text, voice, chat) AI Safety, Alignment & Governance • Develop and deploy RLHF pipelines, guardrails, and hallucination mitigation frameworks • Establish Responsible AI practices, including fairness, explainability, and compliance • Lead AI governance frameworks for enterprise deployments Scalability & Performance Optimization • Architect distributed training and inference pipelines across GPU clusters • Optimize model performance using quantization, mixed precision (FP16/BF16), and MoE architectures • Reduce latency and cost using frameworks like vLLM, Triton, Ray Leadership & Collaboration • Lead and mentor teams of AI engineers, researchers, and architects • Collaborate with product, engineering, and CX teams to deliver AI-powered solutions • Partner with enterprise clients to design and deploy custom AI solutions for contact centers Note Education Master’s or Ph.D. in Computer Science, AI, Machine Learning, or related field preferred.