AI Solution Architect II

Sapsf; United States; Hybrid; USD 150,000 - 210,000; Full time

Job summary

The AI Architect role guides strategy and delivery for interoperable, compliant, and economically viable modern AI solutions. You will architect a Hybrid AI landscape blending Frontier Models with cost-efficient SLMS and edge inference, drawing on OpenAI, NVIDIA, Google, Microsoft and AWS technologies.

You will engage AI and business leaders to shape technology strategy and deliver complex AI initiatives. This leadership role requires deep hands-on expertise in advanced AI and strong business

Qualifications

• 8–10+ years in technical leadership with enterprise-scale cloud architecture.
• Architectural expertise on one primary cloud platform and familiarity with another.
• Experience guiding GenAI solutions using major platforms (Azure OpenAI, Vertex AI, AWS Bedrock).
• Proven track record designing complex RAG pipelines.
• Experience with instruction tuning / fine-tuning for LLMs.
• Strong communication with senior stakeholders on strategy and governance.

Responsibilities

• Define reference architectures for multi-agent systems and guidance on pattern selection.
• Design memory systems including vector databases, knowledge graphs, and episodic memory.
• Define end-to-end RAG pipelines with scalable retrieval and reranking standards.
• Advise on cross-cloud decisions and vendor integrations while managing lock-in risks.
• Establish GenAIOps, CI/CD, IaC, and observability standards with governance metrics.
• Architect enterprise integration with Teams, Dynamics/Salesforce, ERPs and Kubernetes.
• Lead client advisory sessions, roadmapping, and thought leadership activities.
• Public speaking and industry contributions to advance the AI field.

Skills

Enterprise Leadership

Cloud Expertise

GenAI & LLM

RAG Orchestration

Model Fine-tuning

Leadership & Advisory

Tools

LangChain

LlamaIndex

Semantic Kernel

Job description

Role Summary 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

• 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

Skill Requirements

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).