Principal Engineer - AI Engineering

MontyCloud seeks a Principal Engineer - AI Engineering to define the technical vision for production-grade agentic AI systems used in intelligent cloud operations. The role is based at MontyCloud Bangalore Office and is listed as full-time. The engineer will build reliable, observable AI systems at enterprise scale while setting engineering standards and guiding technical strategy.

Responsibilities

• Architect multi-agent systems, orchestration frameworks, MCP server infrastructure, retrieval and memory pipelines, and observability layers. Make architectural decisions for AI platform design, tool integrations, and LLMOps infrastructure; document decisions in Architecture Decision Records and technical standards.
• Design critical AI platform components and establish practices for evaluation, prompt engineering, reliability, governance, and cost optimization. Lead initiatives across teams to improve quality and scalability, and work with platform, infrastructure, and data engineering teams to integrate AI automation into cloud operations.
• Mentor Lead and Staff AI Engineers, review designs and major code contributions, evaluate emerging technologies, and develop prototypes and proposals. Contribute through technical writing, open-source work, or speaking engagements.

Required qualifications and skills:

• 12+ years of overall software engineering experience; prior Principal Engineer or equivalent individual-contributor experience; substantial recent hands-on experience deploying applied AI systems to production; and a record of leading large-scale, cross-team initiatives, architecting enterprise AI platforms and cloud-native workloads, and mentoring senior engineers.
• Experience with production agentic systems, multi-agent orchestration, agent communication, memory and planning, and MCP-based tool integration. Relevant frameworks include LangGraph, Strands Agents, CrewAI, AutoGen, or equivalents.
• Knowledge of prompt governance, evaluation and regression detection, agent tracing, output-quality monitoring, and AI cost governance. Cloud and integration requirements include AWS, AWS Bedrock, AgentCore, Kubernetes, Docker, Terraform, foundation-model APIs, RAG and Graph-RAG, embeddings, retrieval, reranking, and knowledge graphs.
• A bachelor's or master's degree in Computer Science, Artificial Intelligence, Machine Learning, Engineering, or a related technical discipline; equivalent practical experience in advanced AI system design and distributed cloud platforms may be considered.

Preferred

Experience with cloud-operations AI, infrastructure automation, developer tooling, serverless AI deployment, inference cost optimization, model fine-tuning, RLHF, or advanced evaluation; experience at an AI-first or cloud-native product company and contributions to open source, technical publications, talks, or research.