Engagement Architect Agentic Ai Rag

Job Title: Engagement Architect (Agentic AI / RAG)

Location: New York / New Jersey (Onsite)

Job Type: Full-Time

Experience Required: 12+ Years

We are seeking an experienced Engagement Architect with expertise in Agentic AI, Retrieval-Augmented Generation (RAG), and Enterprise AI Architecture. The ideal candidate will lead the architecture, design, governance, and implementation of enterprise-scale AI solutions across AWS and GCP while collaborating with technical stakeholders to deliver secure, scalable, and production-ready AI platforms.

• 12+ years of IT experience with enterprise architecture.
• Strong expertise in Agentic AI architecture and RAG (Retrieval-Augmented Generation/Reasoning).
• Experience with multi-agent orchestration and agent harness design.
• Hands-on experience in spec-driven development and design-to-code conversion.
• Strong knowledge of prompt engineering, evaluation engineering, and reasoning frameworks.
• Experience designing retrieval pipelines, semantic search, and knowledge graph integration.
• Expertise in AWS and Google Cloud Platform (GCP).
• Strong understanding of enterprise security architecture, Identity-as-Code, and Policy-as-Code.
• Experience with design-time and runtime AI governance.
• Knowledge of React-pattern reasoning loops.
• Excellent architecture documentation and stakeholder communication skills.

• Lead end-to-end architecture for enterprise Agentic AI solutions.
• Design and build supervisor, intake, and data-source-level AI agents.
• Implement multi-agent orchestration and reasoning workflows.
• Design, optimize, and tune Retrieval-Augmented Generation (RAG) pipelines.
• Lead prompt engineering and AI model evaluation strategies.
• Ensure secure, scalable AI architecture aligned with enterprise standards.
• Oversee design-to-code conversion, implementation reviews, and testing.
• Create architecture documentation, diagrams, and technical standards.
• Conduct architecture review sessions and collaborate with engineering teams and client stakeholders.
• Drive governance, security, and best practices across AI implementations.