Gen AI Architect

Charlotte, NC Long term Responsibilities • Define and drive the AI/ML architecture and roadmap, including both traditional machine learning and Generative AI (GenAI) use cases. • Design comprehensive end-to-end AI solutions covering data ingestion, feature engineering, model training, inference pipelines, and monitoring frameworks. • Lead the integration of Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG) frameworks, utilizing tools such as LangChain, LangGraph, or similar. • Develop and deliver cutting-edge AI/ML solutions, incorporating genetic AI techniques, innovative design principles, and scalable deployment strategies. • Gain a good understanding of traditional AI/ML approaches and leverage this knowledge to create robust, hybrid solutions. • Collaborate with business stakeholders to translate requirements into scalable AI-driven technical solutions. • Evaluate and select appropriate AI/ML tools, cloud services, frameworks, and libraries based on use case needs and industry best practices. • Ensure models adhere to governance, security, explainability, and regulatory compliance, embedding ethical AI principles into system design. • Guide engineering teams in the implementation of AI components, emphasizing scalability, reliability, and performance optimization. • Partner with DevOps teams to establish CI/CD pipelines for AI, including model versioning, deployment automation, and ongoing A/B testing. • Keep abreast of the latest industry research, breakthroughs, and emerging trends in AI, including tracing frameworks, LLM observability, and other innovative areas, recommending adoption of best practices and solutions. Requirements • Experience in leading AI/ML architecture and strategy in enterprise environments. • Strong expertise in designing and deploying large-scale AI/ML solutions, including LLMs, RAG frameworks, and genetic AI techniques. • Experience with AI/ML tools and frameworks such as TensorFlow, PyTorch, Hugging Face, LangChain, LangGraph, or similar. • Agentic AI experience: design, develop, and deliver tracing frameworks and LLM observability solutions. • Deep understanding of data workflows, feature engineering, model training, evaluation, and deployment. • Good understanding of traditional AI/ML concepts, alongside expertise in generative AI and related frameworks. • Hands-on experience with AI/ML model observability, tracing frameworks, and monitoring solutions. • Knowledge of cloud platforms (AWS, Azure, GCP) and services tailored for AI deployment. • Familiarity with model governance, security, explainability, and ethical AI standards. • Experience in developing CI/CD pipelines for AI/ML, including model versioning, monitoring, and performance tuning. • Strong problem-solving, communication, and stakeholder management skills. Preferred, But Not Required • Advanced degree (Ph.D., Master s) in Computer Science, Data Science, AI, or related fields. • Publications or practical contributions to AI research and open-source projects. • Experience working in regulated industries or environments requiring compliance and governance. • Familiarity with project management and Agile practices.