AI Solutions Architect

• Design and deliver end-to-end GenAI solutions from concept through production.
• Define architectures leveraging LLMs, RAG, agents, and orchestration frameworks.
• Architect multi-model strategies across GPT, Anthropic, Gemini, and related

• Design multi-agent patterns, tool integration frameworks, and orchestration approaches.
• Translate business needs into scalable, reusable architecture patterns.

• Architect solutions primarily on Microsoft Azure, including:

• Design and implement solutions using Microsoft Copilot Studio and extensibility patterns.
• Leverage Google AgentSpace and related agent platforms where appropriate.
• Define integration patterns with enterprise systems and APIs.
• Ensure solutions follow cloud-native, resilient, observable, and cost-aware design principles.

• Apply strong software engineering fundamentals:

• Establish patterns for prompt management, model evaluation, and versioning.
• Implement cost optimization strategies (token management, routing, caching).
• Partner with engineering teams to ensure production-grade implementations.

• Embed security, privacy, compliance, and responsible AI guardrails into architecture.
• Define patterns for human-in-the-loop workflows and risk mitigation.
• Collaborate with security, legal, and risk stakeholders to align with enterprise standards.

• Act as a trusted technical advisor to delivery teams and stakeholders.
• Present architecture options and trade-offs clearly to technical and executive audiences.
• Mentor engineers and architects in GenAI patterns and best practices.
• Contribute to reusable accelerators, reference architectures, and platform standards.

• 5+ years of experience in software engineering, solution architecture, or related

• Demonstrated experience designing and delivering cloud-based solutions in

• Hands-on experience building or architecting Generative AI solutions using LLMs.
• Strong expertise in Azure cloud architecture and services.
• Practical experience with:

• Experience designing secure, scalable, and cost-aware cloud solutions.
• Experience with Microsoft Copilot Studio and Copilot extensibility patterns.

• Experience with token cost modeling and AI performance optimization.

• Knowledge of model evaluation frameworks, benchmarking, and drift monitoring.
• Strong grounding in API design, DevOps practices, and modern engineering patterns.
• Ability to translate business requirements into clear technical designs.
• Excellent communication and stakeholder management skills.

• Experience with Google AgentSpace or other enterprise

• Azure AI Fundamentals and Solution Architect Certifications
• GCP Certifications
• TOGAF
• Experience implementing multi-agent systems in production environments.
• Exposure to enterprise AI governance, compliance, and risk management frameworks.
• Experience contributing to enterprise platform strategy or AI operating models.
• Background in data engineering, embeddings pipelines, or semantic search optimization.

• GenAI solutions move efficiently from PoC to production with clear architectural standards.
• Reusable patterns and accelerators reduce duplication and accelerate delivery teams.
• AI solutions are secure, cost-managed, and aligned with enterprise governance.
• Stakeholders understand both the value and risks of GenAI initiatives.
• The organization's AI capabilities mature through structured architecture

• "route" is used for session stickiness
• "careerSiteCompanyId" is used to send the request to the correct data center
• "JSESSIONID" is placed on the visitor's device during the session so the server can identify the visitor
• "Load balancer cookie" (actual cookie name may vary) prevents a visitor from bouncing from one instance to another

Deloitte