Job Title: Lead AI Engineer (Agentic AI / GenAI / LLMs)
Experience: 12+ Years
Location: Dallas
Role Overview
We are seeking a Lead AI Engineer with 12+ years of experience to lead enterprise-scale Agentic AI and Generative AI initiatives. This role requires a strong mix of AI architecture depth, end-to-end product engineering, and thought leadership. The ideal candidate will work closely with business and leadership teams to translate complex business problems into scalable, cost-efficient Agentic AI architectures and deliver production-grade AI products on cloud platforms.
Key Responsibilities
- Provide thought leadership and partner with leadership and business teams to shape AI strategy and high-impact use cases.
- Translate business problem statements into scalable Agentic AI solution architectures.
- Architect and deliver multi-agent and GenAI solutions using enterprise-grade frameworks.
- Define and build a holistic AI framework/platform for enterprise adoption.
- Lead end-to-end AI product development across backend services, databases, middleware, Python, and agentic workflows.
- Drive cost-efficient and scalable AI solutions, optimizing models, infrastructure, and system design.
- Mentor AI engineers and define technical standards, architectural patterns, and roadmaps.
Required Skills & Experience
- 12+ years of experience in AI/ML engineering or software engineering with significant leadership responsibility.
- Strong hands-on expertise in Agentic AI and multi-agent systems (e.g., CrewAI, LangGraph, or similar frameworks).
- Proven experience with Generative AI and LLMs, including RAG architectures, prompt engineering, model evaluation, and optimization.
- Expert proficiency in Python and solid software engineering fundamentals.
- Strong experience in backend engineering, API design, middleware, and service-based architectures.
- Experience with databases (SQL)
- Strong knowledge of cloud platforms (AWS/Azure/GCP), scalable architecture patterns, and CI/CD pipelines.
- Solid understanding of LLMOps/MLOps, including deployment, monitoring, observability, and lifecycle management.
- Ability to communicate complex AI concepts clearly to business, leadership, and non-technical stakeholders.
Preferred / Nice-to-Have Skills
- Experience designing enterprise AI platforms or internal AI accelerators.
- Knowledge of cost optimization / FinOps strategies for GenAI workloads will be nice to have.
- Experience with vector databases, embeddings, and semantic search systems.
- Understanding of security, governance, and compliance considerations in enterprise AI solutions.
- Exposure to full-stack development and UI integration for AI-powered products.
What Success Looks Like
- Business problems are effectively translated into scalable and production-ready Agentic AI solutions.
- Reusable AI frameworks and accelerators reduce time-to-market across teams.
- AI systems are cost-efficient, observable, secure, and reliable at enterprise scale.
- Engineering teams are guided with clear architecture, standards, and technical vision.