AI Solution Architect

Role Summary

• Lead the design and deployment of end-to-end agentic AI systems, owning the full architecture stackfrom knowledge curation to cognitive reasoning to autonomous execution.
• This role is accountable for building a multi-layered AI system architecture where agents:
• Understand enterprise context (knowledge layer)
• Reason and plan (cognitive layer)
• Execute actions (agentic layer)
• Continuously improve (feedback + performance layer)
• You are not building isolated AI features—you are architecting an enterprise AI operating system.
• Architect and operationalize the full agentic AI stack
• Build reusable AI system layers and components
• Enable scalable, governed, high-performance autonomous enterprise workflows
• Expanded Responsibilities: Full Agentic AI Stack Ownership

1. Knowledge Curation & Semantic Layer

• Define strategy for enterprise knowledge ingestion, curation, and structuring
• Build pipelines for:
• Structured + unstructured data
• Documents, APIs, real-time streams
• Establish:
• Metadata frameworks
• Architecture for consuming ontologies / semantic models
• Ensure knowledge is AI-consumable, contextual, and continuously updated
• Outcome: A trusted, dynamic enterprise knowledge foundation
• Design a cognitive catalog that indexes:
• Agents
• Tools/APIs
• Skills and capabilities
• Enable discoverability and reuse of:
• Prompts
• Workflows
• Models
• Build a system where agents can discover and invoke other agents/tools
• Outcome: A self-service, composable AI capability layer

3. Decisioning Framework

• Creative / Generative Intelligence
• LLM orchestration for:
• Content generation
• Hypothesis creation
• Natural language reasoning
• Manage multi-model strategy (cost vs performance vs specialization)
• Logical / Deterministic Intelligence
• Rule engines, mathematical reasoning, workflow logic
• Integrate with AI/ML models
• Compliance
• Model accuracy
• Hybrid AI systems combining:
• LLM reasoning + programmatic control
• Outcome: Balanced creativity + reliability in AI decisioning

4. Agentic Layer (Autonomous Systems Design)

• Architect:
• Single-agent and multi-agent performant systems
• Hierarchical and collaborative agent models
• Define:
• Planning, memory, and execution loops
• Task decomposition and coordination
• Enable agents to:
• Take actions across enterprise systems
• Learn from feedback
• Outcome: Production-grade autonomous workflows

5. Agentic Integration Layer

• Design integration with:
• Enterprise applications (CRM, ERP, HR systems)
• Data platforms and APIs
• Build secure action frameworks for agents:
• API orchestration
• Event-driven architectures
• Ensure agents can execute real business transactions
• Outcome: AI moves from insight action

6. Data Mesh & Distributed Data Architecture

• Align agentic systems with data mesh principles
• Enable domain-driven data ownership
• Ensure:
• Data discoverability
• Data product standardization
• Integrate with platforms like:
• Databricks
• Snowflake
• Outcome: Scalable, domain-aligned data foundation for AI

7. Governance, Security & Control Framework

• Define governance for:
• Autonomous decision-making
• Data access and privacy
• Implement:
• Role-based access controls for agents
• Human-in-the-loop mechanisms
• Audit trails and explainability
• Ensure compliance with enterprise and regulatory standards
• Outcome: Trusted and controllable AI systems

8. Performance, FinOps, Observability & Optimization

• Define and track:
• Task success rate
• Agent autonomy levels
• Cost per execution
• Latency and throughput
• Build observability stack for:
• Agent behavior
• Failure modes
• Optimize using:
• Feedback loops
• Continuous learning systems
• Outcome: Reliable, efficient, and scalable AI operations

9. Platform Engineering & Reusable Frameworks

• Build Agentic AI development platform with reusable:
• Agent frameworks & templates 
• Agent repository and discoverability
• Orchestration layers
• Governance layers
• SDKs and accelerators
• Productize Agentic capabilities into:
• Client-facing offerings
• Repeatable solutions
• Outcome: IP-led AI engineering business

Must-Have

• 15+ years in distributed systems, AI/ML, or platform engineering
• Deep hands-on experience building:
• LLM-based systems
• Agentic or workflow automation platforms
• Proven experience delivering enterprise-scale AI systems in production
• Critical Differentiators
• Has architected multi-layer AI systems (not just apps)
• Experience with:
• Knowledge systems (RAG, ontologies)
• Multi-agent orchestration
• AI governance frameworks
• Strong engineering depth + business acumen