Gen AI Lead/Architect with AWS Experience

The role requires strong hands-on experience with Agentic AI, LLMs, RAG, vector search, AWS, and enterprise system integration, along with the ability to take solutions from architecture through production. Design agentic workflows integrating LLMs with APIs, databases, backend services, enterprise applications, and MCP servers.

Role Name: Gen AI Lead/Architect with AWS Experience
Location: Dallas, TX. ONSITE
Job Description:
JOB Description: Seeking a Store AI Agent Lead to architect, build, and scale AI-powered agents supporting store operations. The role requires strong hands-on experience with Agentic AI, LLMs, RAG, vector search, AWS, and enterprise system integration, along with the ability to take solutions from architecture through production.
Key Responsibilities
Lead the end-to-end architecture and implementation of Store AI Agents.
Design agentic workflows integrating LLMs with APIs, databases, backend services, enterprise applications, and MCP servers.
Build and optimize RAG solutions, including embeddings, retrieval, vector search, grounding, and context management.
Define the process for onboarding and integrating new AI agents, including testing, deployment, monitoring, and production readiness.
Establish clear architecture between LLM reasoning/generation and deterministic backend services.
Leverage Lakehouse/Medallion architecture (Bronze, Silver, Gold) to support AI and data use cases.
Define scalable approaches for AI/RAG testing, validation, observability, and performance optimization.
Address production challenges including latency, context limitations, retrieval quality, scalability, and reliability.
Provide technical leadership across AI/ML, data engineering, cloud, application, and business teams.
Required Skills & Experience
Proven experience delivering at least one end-to-end LLM, RAG, or Agentic AI project.
Strong architectural and hands-on experience with Generative AI on AWS.
Hands-on knowledge of LLMs, RAG, embeddings, vector search, and agentic workflows.
Experience integrating AI agents with APIs, databases, backend services, and enterprise systems.
Understanding of MCP (Model Context Protocol) and agent-to-tool/service integration.
Experience with Lakehouse and Medallion Architecture and data pipeline optimization.
Strong understanding of AI testing, observability, security, scalability, latency, and production performance.
Ability to articulate end-to-end architecture, technology choices, integration patterns, and design trade-offs.
Preferred
Experience with AWS-based GenAI and data platforms.
Experience building enterprise-scale AI agents or multi-agent solutions.
Exposure to modern vector databases, Lakehouse technologies, and AI orchestration frameworks.
Strong technical leadership and stakeholder communication skills.