Hiring: Gen AI Architect at Charlotte, NC

Role:
Gen AI Architect

Location:
Charlotte, NC (Hybrid 3 days in the office a week)

Duration:
Long-Term Contract

Responsibilities:

• Define the architecture and technical strategy for Generative AI, Agentic AI, LLM, and ML solutions.
• Design and implement single-agent and multi-agent systems for enterprise use cases.
• Develop AI agents capable of reasoning, planning, task execution, tool calling, and workflow orchestration.
• Design and implement Retrieval-Augmented Generation solutions using structured and unstructured enterprise data.
• Integrate AI agents with APIs, databases, microservices, SaaS platforms, and business applications.
• Lead the selection, evaluation, fine-tuning, deployment, and optimization of LLMs and ML models.
• Establish MLOps and LLMOps practices covering CI/CD, model versioning, monitoring, testing, and governance.
• Implement AI guardrails to address hallucinations, prompt injection, data leakage, unauthorized actions, and unsafe outputs.
• Define evaluation and observability frameworks to measure model quality, agent performance, accuracy, latency, cost, and reliability.
• Lead architecture reviews, proof-of-concepts, production deployments, technical documentation, and mentoring of AI and ML engineering teams.

Requirements:

• Bachelor's or master's degree in Computer Science, Artificial Intelligence, Machine Learning, Data Science, or a related field.
• 8+ years of experience in Machine Learning Engineering, AI Engineering, Software Engineering, or Technology Architecture.
• 3+ years of hands-on experience designing and implementing Generative AI and LLM-based solutions.
• Proven experience designing and delivering Agentic AI, autonomous agents, or multi-agent systems.
• Strong background in developing, deploying, serving, and monitoring machine learning and deep learning models.
• Advanced programming experience in Python and familiarity with software engineering best practices.
• Hands-on experience with ML frameworks such as PyTorch, Tensor Flow, Scikit-learn, or Hugging Face.
• Strong knowledge of LLMs, RAG, prompt engineering, context engineering, embeddings, vector databases, and tool/function calling.
• Experience with cloud platforms such as AWS, Microsoft Azure, or Google Cloud, along with APIs, microservices, containers, and CI/CD.
• Knowledge of MLOps, LLMOps, AI security, responsible AI, model governance, data privacy, and production AI system monitoring.