Lead AI Engineer

Lead AI Engineer

Location: Charlotte (NC) or Dallas, TX or SFO onsite

Contract: 18+ Month

Job Description:

5+ years of overall software engineering experience, or equivalent demonstrated through work experience, training, military experience, or education.

3+ years of hands-on experience building and deploying AI/ML or Generative AI solutions in production environments.

Strong experience with:

• Retrieval-Augmented Generation (RAG)
• LLM orchestration and prompt engineering
• Agentic or workflow-based AI systems
• Proficiency in one or more programming languages such as Python, Java, or similar, with production-grade coding practices.
• Partner with business stakeholders, product owners, and engineering teams to understand business problems, identify AI-enabled opportunities, and define end-to-end technical solutions.
• Design, prototype, and deliver Al-driven workflows, agents, copilots, and automatons using large language models (LLMs) and enterprise AI services.
• Integrate Al capabilities with enterprise platforms and systems (e.g., ServiceNow, Salesforce, data platforms, internal services) using secure APIs and orchestrating patterns.
• Rapidly iterates on prototypes and transiting them into production-ready solutions that meet enterprise standards for reliability, scalability, and supportability.
• Act as a technical bridge between business, product, data, security, and engineering teams to ensure solutions are usable, compliant, and aligned with business objectives.

Lead solution architecture and design activities, including:

• Prompt engineering and Al workflow design
• API integration and service orchestrating
• Enterprise knowledge and data integration
• Security, privacy, risk, and governance considerations
• Own solutions across the full lifecycle—from concept and proof of value through production deployment and continuous improvement.
• Apply and promote best practices for responsible AI, including model risk management, data protection, and compliance with enterprise and regulatory requirements.
• Contribute to the development of reusable patterns, standards, and guidance to support scalable Al adoption across the enterprise.