We are seeking an experienced AI Architect to lead the design and implementation of scalable AI solutions, autonomous AI systems, and intelligent agent-based platforms. The ideal candidate will have deep expertise in Generative AI, Agentic AI, Large Language Models (LLMs), and enterprise AI architecture. The role requires collaboration with business stakeholders, engineering teams, data scientists, and cloud architects to define AI strategy, solution architecture, governance, and enterprise AI adoption.
Key Highlights
Key Responsibilities
Technical Skills Required
Benefits & Perks
Job Description
Dice is the leading career destination for tech experts at every stage of their careers. Our client, INUIX Consulting, is seeking the following. Apply via Dice today!
Hello All,
We are hiring..................
Role: AI Architect
Location: Dallas, TX (Hybrid) Relocation is must if not local or near
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We are seeking an experienced AI Architect with deep expertise in Generative AI, Agentic AI, Large Language Models (LLMs), and enterprise AI architecture. The ideal candidate will lead the design and implementation of scalable AI solutions, autonomous AI systems, and intelligent agent-based platforms that solve complex business problems.
The AI Architect will work closely with business stakeholders, engineering teams, data scientists, and cloud architects to define AI strategy, solution architecture, governance, and enterprise AI adoption.
Key Responsibilities
- Design and architect enterprise-scale AI/ML and Agentic AI solutions.
- Build intelligent AI agent ecosystems, autonomous workflows, and multi-agent architectures.
- Define architecture for LLM-based applications using RAG, vector databases, orchestration frameworks, and AI workflow engines.
- Lead end-to-end AI solution design including scalability, security, reliability, and performance.
- Collaborate with business and technical teams to translate requirements into AI architecture and implementation plans.
- Design AI integration strategies with enterprise systems, APIs, databases, and cloud platforms.
- Define AI governance, responsible AI practices, security, and compliance standards.
- Evaluate emerging AI technologies, frameworks, and tools for enterprise adoption.
- Provide technical leadership and mentorship to AI engineers and development teams.
- Support AI model deployment, monitoring, optimization, and production operations.
- Drive architecture reviews, proof of concepts (POCs), and innovation initiatives.
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