Agentic AI Principle Engineer

Key Responsibilities:Design, build, and scale production-grade agentic orchestration systems managing:Multi-agent workflowsTool-calling frameworksAutonomous decision-making loops at enterprise scaleArchitect and develop a centralized multi-agent platform (hub) to be consumed across cross-functional teamsDefine platform standards, agent registries, and release governanceBuild and optimize RAG (Retrieval-Augmented Generation) pipelines including:Semantic retrieval mechanismsGrounding strategiesRobust failure handling across large-scale LLM interactionsEnable agent-to-agent coordination layers for complex workflows and distributed intelligenceWork closely with teams to integrate Claude-based coding workflows into development pipelinesRequired Technical Skills:Hands-on experience with at least one or more frameworks:LangGraphLangChainLlamaIndexAutoGenCrewAIMCP (Model Context Protocol)Strong experience building real-world production deployments (not just POCs)Deep understanding of:Multi-agent architecturesLLM orchestrationTool integrations and API chainingCloud & Platform Expertise:Proven experience deploying solutions on:Azure or Google Cloud Platform (GCP)Strong knowledge of:Distributed systems designScalable microservices architectureAI Engineering & Governance:Experience implementing:Production-grade observability (logging, tracing, monitoring for LLM systems)Automated evaluation pipelines for model/agent performancePII protection and data governance controlsResponsible AI guardrails integrated into SDLCNice to Have:Experience building enterprise AI platforms or internal developer platformsExposure to high-scale LLM workloads (millions of interactions)Knowledge of AI safety, compliance, and ethical AI practices