Agentic AI Engineering & Autonomous Systems Architect

Role Summary Lead the design and deployment of end-to-end agentic AI systems, owning the full architecture stack—from knowledge curation to cognitive reasoning to autonomous execution. This role is accountable for building a multi-layered AI system architecture where agents: o Understand enterprise context (knowledge layer) o Reason and plan (cognitive layer) o Execute actions (agentic layer) o Continuously improve (feedback + performance layer) o You are not building isolated AI features—you are architecting an enterprise AI operating system. Core Mandate 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: o Structured + unstructured data o Documents, APIs, real-time streams Establish: o Metadata frameworks o Architecture for consuming ontologies / semantic models o Ensure knowledge is AI-consumable, contextual, and continuously updated o Outcome: A trusted, dynamic enterprise knowledge foundation 2. Enterprise AI Index of Capabilities Design a cognitive catalog that indexes: o Agents o Tools/APIs o Skills and capabilities Enable discoverability and reuse of: o Prompts o Workflows o Models o 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 o LLM orchestration for: Content generation Hypothesis creation Natural language reasoning o Manage multi-model strategy (cost vs performance vs specialization) Logical / Deterministic Intelligence o Rule engines, mathematical reasoning, workflow logic o Integrate with AI/ML models o Compliance o Model accuracy Hybrid AI systems combining: o LLM reasoning + programmatic control Outcome: Balanced creativity + reliability in AI decisioning 4. Agentic Layer (Autonomous Systems Design) Architect: o Single-agent and multi-agent performant systems o Hierarchical and collaborative agent models Define: o Planning, memory, and execution loops o Task decomposition and coordination Enable agents to: o Take actions across enterprise systems o Learn from feedback Outcome: Production-grade autonomous workflows 5. Agentic Integration Layer Design integration with: o Enterprise applications (CRM, ERP, HR systems) o Data platforms and APIs Build secure action frameworks for agents: o API orchestration o Event-driven architectures o 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: o Data discoverability o Data product standardization Integrate with platforms like: o Databricks o Snowflake Outcome: Scalable, domain-aligned data foundation for AI 7. Governance, Security & Control Framework Define governance for: o Autonomous decision-making o Data access and privacy Implement: o Role-based access controls for agents o Human-in-the-loop mechanisms o Audit trails and explainability o Ensure compliance with enterprise and regulatory standards Outcome: Trusted and controllable AI systems 8. Performance, FinOps, Observability & Optimization Define and track: o Task success rate o Agent autonomy levels o Cost per execution o Latency and throughput Build observability stack for: o Agent behavior o Failure modes Optimize using: o Feedback loops o Continuous learning systems Outcome: Reliable, efficient, and scalable AI operations 9. Platform Engineering & Reusable Frameworks Build Agentic AI development platform with reusable: o Agent frameworks & templates o Agent repository and discoverability o Orchestration layers o Governance layers o SDKs and accelerators Productize Agentic capabilities into: o Client-facing offerings o Repeatable solutions Outcome: IP-led AI engineering business Must-Have o 15+ years in distributed systems, AI/ML, or platform engineering o Deep hands-on experience building: o LLM-based systems o Agentic or workflow automation platforms o Proven experience delivering enterprise-scale AI systems in production Critical Differentiators o Has architected multi-layer AI systems (not just apps) o Experience with: Knowledge systems (RAG, ontologies) Multi-agent orchestration AI governance frameworks o Strong engineering depth + business acumen