AIA - Principal Architect

Key Responsibilities

• Design multi-agent systems using frameworks like LangGraph, Crew.ai, Strands SDK, Microsoft Agent Framework or Google ADK with clear agent contracts, guardrails, observability and governance
• Provide pre-sales solution support for RFIs/RFPs, Capability/PoV presentations and customer workshops related to GenAI/Agentic AI use cases
• Provide technical advisory on Agentic AI topics such as orchestration patterns, evaluation, observability, AgentOps, context engineering etc. to customers
• Architect context engineering patterns covering semantic layers, vector stores, metadata catalogs, lineage and knowledge graphs to setup data for AI Agents consumption
• Demonstrate technical authority and thought leadership in GenAI/Agentic AI discussions
• Lead architecture reviews and make technology selection decisions across the stack
• Leverage coding agents to experiment and build AI Agent prototypes to demonstrate tools & design patterns
• Create architecture decision frameworks, technology comparison matrices and visual slide decks to communicate complex architecture and technical strategies to mixed audience (executive + engineering)
• Mentor engineering and sales teams on agentic patterns, context engineering and the evolving AI services landscape
• Provide technology consulting support to delivery, CoE/community by publishing best practices/architectural guidelines, providing training, publishing whitepapers/case studies and building reusable components.

Requirements

• Overall, 15 + years of experience in the Data Engineering and/or AIML Space
• 2+ years of experience in Generative AI/Agentic AI architecture & implementation
• 2+ years of experience in pre-sales solutioning in GenAI/Agentic AI space
• Strong proficiency in one or more Agentic AI frameworks (LangGraph, Crew.ai, Strands SDK, Google ADK, Microsoft Agent Framework)
• Strong proficiency in foundational models and AI/GenAI Services in one of the hyperscalers (AWS, Azure, GCP)
• Deep expertise in architecting & implementing multi-agentic systems with appropriate guardrails, observability and governance
• Deep expertise in advanced prompt engineering, RAG implementation and AI Agents evaluation techniques
• Prior experience in building ontologies and knowledge graphs is an added advantage