AI Solution Architect I

As an AI Architect, you will guide the strategy and delivery for interoperable, compliant, and economically viable modern AI solutions. You will 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

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.

Do you have experience in NIST standards?, Do you have a Master’s degree?, 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. Preferred Qualifications (The Differentiators) * Multi-Agent Systems: Deep understanding of, and experience designing or prototyping, advanced multi-agent systems (e.g., task decomposition, collaborative agents). * Multi-Cloud Experience: hands-on architectural expertise across all three major clouds (Azure, AWS, GCP). * GenAI Ops & Governance: Hands-on experience with GenAI Ops tooling. Familiarity with AI governance frameworks (NIST AI RMF, ISO 42001) and their practical application. And AI FinOps & Model Routing * Framework Expertise: Hands-on development experience with one or more orchestration frameworks (e.g., LangChain, LlamaIndex, Semantic Kernel). * Thought Leadership & Open Source: Published work (whitepapers, patents), conference speaking engagements, or active contributions to relevant open-source projects. * Certifications: Professional-level cloud certifications (e.g., Azure Solutions Architect Expert, AWS Solutions Architect Professional, GCP Professional Cloud Architect).