Design reusable patterns for Agentic AI systems including RAG, Multi-Agent Orchestration, and Human-in-the-loop systems Define how different agents communicate, share state, and hand off tasks to one another Architect long-term and episodic memory layers using Vector Databases, embedding pipelines, and knowledge graphs Decide when to use high-reasoning models vs. worker models to optimise cost and performance Predict and control token usage architect systems with semantic caching to prevent redundant LLM spend Set architectural standards for explainability, auditability, and guardrails to prevent hallucinations and bias Ensure data governance, privacy compliance, and responsible AI practices across all systems AI Infrastructure & MLOps
Design scalable AI infrastructure including model serving, inference architecture, AI microservices, and APIs Architect distributed systems supporting AI workloads Define MLOps and CI/CD pipelines for AI systems Architect containerised and cloud-nativ...