AI Solution Architect

CEI AI; Chennai District; Hybrid; INR 4,000,000 - 7,000,000; Full time

Job summary

CEI AI is seeking a senior architect to lead the design and deployment of end-to-end agentic AI systems. You will own the full architecture stack from knowledge curation to cognitive reasoning to autonomous execution, building reusable layers and secure integrations with enterprise apps.

The role focuses on creating a self-service, composable AI capability platform, enabling scalable, governed workflows across CRM, ERP, and data platforms.

Qualifications

• 15+ years in distributed systems, AI/ML, or platform engineering
• Deep hands-on experience building LLM-based systems
• Experience with knowledge management, RAG, ontologies

Responsibilities

• Lead the design and deployment of end-to-end agentic AI systems
• Architect an enterprise AI operating system with multi-layer architecture
• Develop reusable AI system layers and components
• Enable scalable, governed autonomous enterprise workflows
• Own the full Agentic AI Stack and integration with enterprise apps

Skills

Distributed systems

AI/ML fundamentals

LLM-based systems

Multi-agent orchestration

Knowledge management

Tools

Databricks

Snowflake

Job description

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-agentperformantsystems
• 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-drivenarchitectures
• 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 AIdevelopment platformwith 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