Systems Architect

Role Overview: The AI Agentic Architect defines enterprise vision, target architectures, and operational foundations for autonomous AI agent ecosystems and LLM orchestration platforms. This leader steers multi-agent frameworks, cognitive workflows, retrieval-augmented generation (RAG), and enterprise tool integrations, ensuring that autonomous agent solutions are robust, compliant, scalable, and strategically aligned with organizational objectives.

Key Responsibilities & Architectural Oversight

• Formulate holistic architectural blueprints for multi-agent autonomous workflows, dynamic task planners, agent memory topologies, and tool-use protocols.
• Architect high-throughput, low-latency LLM orchestration pipelines leveraging LangChain, LangGraph, AutoGen, CrewAI, Semantic Kernel, and custom agents.
• Design advanced RAG pipelines incorporating semantic vector stores (Pinecone, Weaviate, Milvus, Qdrant), hybrid search, re-ranking, and chunking strategies.
• Establish enterprise AI safety frameworks, guardrails (NeMo, Llama Guard), prompt injection defense, hallucination mitigations, and PII anonymization.
• Define agent evaluation protocols, ground truth benchmarking, automated evals, and performance observability across token usage, cost, and latency.
• Direct enterprise integration patterns connecting autonomous agents with ERP, CRM, internal REST APIs, SQL databases, and enterprise data warehouses.
• Guide infrastructure sizing, containerized orchestration on Kubernetes, GPU compute optimization, and cloud provider deployments across Azure, AWS, and GCP.
  
  

Governance & Strategic Direction

• Formulate organizational standards for model selection, SLMs vs. LLMs, fine-tuning methodologies, context window management, and fallback mechanisms.
• Establish comprehensive architectural governance, reviewing agent capabilities, decision trees, permissions, and human-in-the-loop escalation paths.
• Conduct threat modeling and risk assessments for agent autonomous execution, mitigating unintended side effects and ensuring auditability.
  
  

Stakeholder Alignment & Innovation

• Advise executive leadership and business stakeholders on generative AI opportunities, technical feasibility, ROI analysis, and risk tradeoffs.
• Partner with security, legal, and compliance officers to guarantee compliance with global AI regulations, data privacy mandates, and corporate policies.