AI Solution Architect II (IN)

What you’ll do Multi-Agent Architecture Patterns: Define and govern reference architecture for multi-agent systems, covering hierarchical, peer-to-peer, and sequential (ReAct) orchestration models. Guide teams on pattern selection based on client use cases. Memory System Design: Architect the standards for integrated memory systems, including short-term session state, long-term knowledge via vector databases and knowledge graphs, and episodic/audit memory. Ensure coherent retrieval strategies across layers. Retrieval-Augmented Generation (RAG) and CAG (Cache Augmented) Architecture: Define architectural patterns for end-to-end RAG pipelines, including chunking, embedding, vector search (e.g., Azure Cognitive Search, pgvector), and reranking. Ensure designs include standards for lineage, observability, and evaluation (e.g., RAGAS). Cross-Cloud & Vendor Integration: Create and maintain decision frameworks for platform selection (e.g., Copilot Studio for Teams integration, Vertex AI for GCP workloads). Advise clients on balancing vendor lock-in risks with integration benefits. GenAIOps & Observability: Define the architectural standards for GenAIOps, including CI/CD, IaC, and observability. Establish standard metrics to track agent decision traces, latency, token consumption, hallucination, and cost. Safety, Security & Governance: Architect enterprise-wide guardrails for safety (hallucination mitigation), security (prompt injection defense, PII masking), and fairness (bias detection). Apply governance frameworks (NIST AI RMF, ISO 42001) and design human-in-the-loop (HITL) workflows. Enterprise Integration & Scalability: Architect scalable integration patterns for agentic systems with enterprise platforms (Microsoft Entra ID, Teams, Dynamics/Salesforce, ERPs) and compute (Kubernetes). Ensure patterns address security, data residency, and compliance. 1 Technical Leadership & Client Advisory: Combine technical architecture with strategic guidance. Lead C-level workshops on Agentic AI adoption, advise on roadmaps, and shape technology strategy. Mentor other architects and contribute to industry thought leadership. Thought leadership and public speaking: Present at industry events and publish articles that advance the AI industry. Core Qualifications (The Bar) •Enterprise Experience: 8–10+ years in technical leadership, with a strong background in both software engineering and enterprise-scale cloud architecture. •Cloud Expertise: Architectural expertise with one primary cloud platform (Azure, GCP, or AWS) and hands-on familiarity with at least one other. •GenAI & LLM Depth: Demonstrated experience architecting and guiding solutions using GenAI platforms (e.g., Azure OpenAI, Vertex AI, or AWS Bedrock). •RAG & Orchestration: Proven experience designing complex RAG pipelines. •Model Fine-tuning: Experience with instruction tuning or fine-tuning strategies for LLMs. •Leadership & Advisory Skills: Exceptional communication skills with demonstrated experience advising senior stakeholders (Director/C-Level) on technical strategy, roadmaps, and governance.