AI Solution Architect II (116997)

Job Summary

Key Responsibilities

• 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.