Job Title: Gen AI Architect
Location: Onsite, Dallas, TX
Long term
Job ID: 26-39264
Job Description
seeking an experienced Generative AI Architect to lead the design, development, and implementation of enterprise-scale AI solutions. This role requires a strong combination of AI architecture expertise, hands-on software development experience, and business acumen to identify, prioritize, and deliver GenAI use cases that drive measurable business value.
The ideal candidate will work closely with business stakeholders, product teams, and engineering organizations to translate business challenges into scalable AI solutions leveraging Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), AI agents, and modern cloud-based AI platforms.
Key Responsibilities
AI Strategy & Use Case Identification
- Partner with business leaders to identify, evaluate, and prioritize GenAI use cases across the organization.
- Conduct discovery workshops to understand business processes, pain points, and automation opportunities.
- Define AI roadmaps, business cases, ROI metrics, and implementation strategies.
- Assess feasibility, risks, and technical requirements for AI initiatives.
Solution Architecture
- Design end-to-end GenAI architectures using cloud-native and enterprise AI platforms.
- Architect solutions utilizing:
- Large Language Models (OpenAI, Azure OpenAI, Claude, Gemini, Llama)
- Retrieval-Augmented Generation (RAG)
- AI Agents and Agentic Workflows
- Vector Databases
- Knowledge Graphs
- Prompt Engineering Frameworks
- Develop scalable and secure AI integration patterns with enterprise applications and data ecosystems.
- Establish architecture standards, governance models, and AI best practices.
Hands-On Development Leadership
- Provide technical leadership and actively contribute to development efforts.
- Build and prototype AI-powered applications, copilots, chatbots, and intelligent automation solutions.
- Develop APIs, microservices, and backend services supporting AI initiatives.
- Lead implementation of:
- LangChain
- LangGraph
- Semantic Kernel
- LlamaIndex
- AutoGen
- CrewAI or similar agent frameworks
- Guide engineering teams through solution development, testing, and deployment.
Data & Platform Architecture
- Design data pipelines supporting AI applications.
- Collaborate with data engineering teams to establish vector search and document processing frameworks.
- Implement secure data governance, compliance, and responsible AI practices.
- Optimize model performance, latency, scalability, and cost.
Stakeholder Engagement
- Present technical solutions and AI strategy to executive leadership.
- Mentor developers, architects, and engineering teams on GenAI best practices.
- Collaborate across product, engineering, security, and business teams.