AI Architect

Role Summary

As an AI Architect, you will guide the strategy and delivery for interoperable, compliant, and economically viable modern AI solutions. You will design architectures across a Hybrid AI landscape, blending Frontier Models (Azure OpenAI/Gemini) with Cost-Efficient Small Language Models (SLMs) and Edge Inference.

You will craft solutions based on advanced AI technologies from OpenAI, NVIDIA, Google, Microsoft, and AWS. This role has a focus on enabling advanced Agentic AI solutions that transform core business functions and enable the future hybrid workforce.

Working at the highest levels, you will engage AI, Technology, and Business leaders in the world’s most successful organizations. You will lead architectural design, establish best practices, for our most complex AI initiatives.

This role requires a combination of deep hands-on technical expertise in advanced AI with strategic business acumen, serving as both a technical authority and a trusted advisor to clients.

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.
• 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.