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
- Architecture for consuming ontologies / semantic models
- Ensure knowledge is AI-consumable, contextual, and continuously updated
- Outcome: A trusted, energetic 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: Bal
📌 AI Solution Architect (Chennai)
🏢 CEI AI
📍 Chennai
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