Lead AI Engineer (Agentic AI / GenAI / LLMs)

Anblicks; Dallas (TX); On-site; USD 140,000 - 180,000; Full time

Job summary

Anblicks is looking for a Lead AI Engineer based in Dallas, Texas, to lead enterprise-scale initiatives in Agentic AI and Generative AI. The ideal candidate will have over 13 years of experience in AI/ML engineering and demonstrate strong expertise in developing scalable AI architectures. You will work closely with leadership teams to translate business needs into AI solutions, mentoring engineers along the way. Candidates should be proficient in Python and have knowledge of cloud platforms like AWS, Azure, and GCP.

Qualifications

• 13+ years of experience in AI/ML engineering or software engineering with leadership responsibility.
• Strong hands-on expertise in Agentic AI and multi-agent systems.
• Proven experience with Generative AI and LLMs, including RAG architectures.
• Expert proficiency in Python and solid software engineering fundamentals.
• Strong experience in backend engineering, API design, and middleware.

Responsibilities

• Lead enterprise-scale Agentic AI and Generative AI initiatives.
• Architect and deliver multi-agent and GenAI solutions using frameworks.
• Provide thought leadership and shape AI strategies.
• Mentor AI engineers, defining technical standards and roadmaps.

Skills

Agentic AI and multi-agent systems

Generative AI and LLMs

Python

Backend engineering

Database (SQL)

Cloud platforms (AWS/Azure/GCP)

LLMOps/MLOps

API design

Cost optimization / FinOps

Full-stack development

Job description

Job Title: Lead AI Engineer (Agentic AI / GenAI / LLMs)

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

• 13+ 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.