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. Apply and promote best practices for responsible Al, including model risk management, data protection, and compliance with enterprise and regulatory requirements.