Principal AI Solution Architect (Agentic Systems)

Experience: 13 Years (3 in Generative AI Architecture)

The Vision

As a Principal AI Solution Architect you will lead the technical vision for our most complex enterprise AI engagements. You wont just choose a model; you will architect the Sovereign AI infrastructure multi-agent orchestration layers and the governance frameworks that allow companies to move from experimental assistants to autonomous Agentic workforces.

Strategic Responsibilities

• Architecture Blueprinting: Define the target-state architecture for multi-agent systems including decision-making on component selection (Orchestrator vs. Routers) cloud topology (Public Hybrid or Air-gapped) and deployment models.
  
• Agentic Design Patterns: Implement advanced patterns like Interleaved Decomposition (Plan-Act-Reflect) and Multi-Agent Collaboration to solve non-linear high-stakes business processes.
  
• Enterprise Integration (The "Action" Layer): Design standard integration patterns to bridge LLMs with legacy ERPs SAP and custom data lakes.
  
• Governance & Trust: Build the Safety Scaffolding including guardrails PII masking and automated LLM-as-a-Judge evaluation pipelines to ensure compliance in regulated sectors (Pharma Finance 5G).
  
• Cost & Performance Optimization: Architect for Smarter not Larger Implement strategies for context compression model routing (small-model vs. large-model logic) and semantic caching to maximize ROI.
  
• Leadership & Mentorship: Lead discovery sessions with C-suite stakeholders manage technical risk across globally distributed engineering teams and mentor Senior AI Engineers on best practices.
  

Core Technical Stack & Expertise

• Orchestration: Expert-level mastery of LangGraph Semantic Kernel or Autogen.
  
• Frameworks: Deep experience with Pydantic AI LangChain and LlamaIndex for stateful complex RAG and Agentic workflows.
  
• Infrastructure: Proficiency in Kubernetes Docker and AI gateways and experience with Sovereign AI tools.
  
• Data Strategy: Advanced knowledge of Vector DBs (Pinecone Milvus) Graph DBs (Neo4j) and hybrid search strategies.
  

Requirements

Qualifications

• Proven Track Record: You have successfully led at least two enterprise-grade AI projects from initial architecture through to global production deployment.
  
• Consultative Mindset: Ability to translate vague business requirements into a robust technical roadmap and a "Build vs. Buy" analysis.
  

• Bachelors or Masters in CS AI or a related field. Professional
  
• Certifications in AWS/Azure/GCP AI Architecture are highly preferred.