Principal Engineer - AI Engineering

Role Overview As a Principal AI Engineer at MontyCloud, you will define and drive the technical vision for agentic AI systems powering the next generation of intelligent cloud operations. This role focuses on architecting scalable, production-grade AI systems, establishing engineering standards, mentoring senior engineers, and leading strategic technical initiatives across the organization. You will work at the intersection of AI, cloud infrastructure, and autonomous operations to build systems that are reliable, observable, and capable of operating at enterprise scale. Key Responsibilities Technical Leadership & Architecture Define and own the technical vision for agentic AI systems across the platform Architect scalable multi-agent systems, orchestration frameworks, MCP server infrastructure, retrieval and memory pipelines, and observability layers Drive architectural decisions related to MCP/ tool ecosystems, AI platform design, and LLMOps infrastructure Evaluate emerging AI technologies, frameworks, and models to influence engineering and product roadmaps Create and maintain Architecture Decision Records (ADRs) and technical standards Engineering & Delivery Design and develop critical AI platform components and infrastructure Establish AI engineering best practices and discipline across the organisation - design patterns, evaluation practices, prompt engineering, reliability standards, governance, and cost optimization Lead cross-functional technical initiatives to improve AI system quality, reliability and scalability Collaborate with platform, infrastructure, and data engineering teams to embed AI-driven automation into cloud operations workflows Mentorship & Technical Community Mentor Lead and Staff AI Engineers through architecture reviews, design discussions, and problem-solving sessions Conduct rigorous technical reviews of designs, architectures, and major code contributions Contribute to MontyCloud’s technical brand through technical writing, open-source contributions, or speaking engagements Innovation & Strategic Impact Identify opportunities where agentic AI can create significant product or operational improvements Build prototypes, technical proposals, and proof-of-concepts to validate new ideas Stay current with advancements in AI research, agentic frameworks, and LLMOps practices Desired Skills and Requirements Must Have Agentic AI & Multi-Agent Systems Production-grade agentic AI system design and development Agentic AI System Design & Architecture - Multi-agent architectures and orchestration, Agent-to-agent communication, Agent memory and planning strategies, Tool integration and MCP server design Agent orchestration frameworks - LangGraph, Strands Agents, CrewAI, AutoGen, or equivalent agentic AI frameworks LLMOps & AI Platform Engineering AI Governance & Lifecycle Management - Prompt versioning and governance, evaluation frameworks, regression detection AI Observability &…