Job Title: Engagement Architect (Agentic AI / RAG)
Location: New York / New Jersey (Onsite)
Job Type: Full-Time
Experience Required: 12+ Years
Job Summary: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.
Required Skills:- 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.
Responsibilities:- 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.