Job Description
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 ...