Staff Engineer - AI Engineering

MontyCloud Staff Engineer - AI Engineering 8 - 10 YOE MontyCloud Bangalore Office Full-Time Role OverviewAs a Staff AI Engineer at MontyCloud, you will design, build, and operate production-grade agentic AI systems powering intelligent cloud operations at scale. This role focuses on developing scalable AI agents, orchestration pipelines, and cloud-native AI infrastructure while contributing to engineering standards, reliability, and operational excellence across the platform. You will work at the intersection of AI, cloud infrastructure, and autonomous operations to deliver systems that are reliable, observable, secure, and production-ready.Key ResponsibilitiesEngineering & DeliveryArchitect, build, and operate production-grade AI agents and multi-agent systems for cloud management use casesDesign and own AI inference and orchestration pipelines optimized for scalability, latency, reliability, and cost efficiencyBuild safety and reliability guardrails for autonomous AI systems operating on live cloud infrastructureDevelop human-in-the-loop workflows, rollback strategies, scoped permissions, and audit mechanisms for AI-driven operationsCollaborate with platform, infrastructure, and data engineering teams to embed AI-native automation into cloud management workflowsStandards & Technical QualityImplement observability and monitoring for agentic systems, including agent tracing, MCP interaction auditing, output quality monitoring, and cost governanceContribute to engineering standards for agentic design patterns, agentic AI architectures, MCP server and tool design, Prompt engineering, RAG and Graph-RAG pipelines, LLMOps practices, and Foundation model integrations.Conduct rigorous technical reviews of AI architectures, systems, and features to improve engineering quality and reliabilityDocument technical decisions, trade-offs, and implementation patterns clearly for broader engineering adoptionInnovation & Opportunity IdentificationIdentify opportunities where agentic AI can improve product capabilities or operational efficiencyBuild proof-of-concepts and prototypes to validate technical feasibility and scalabilityEvaluate emerging AI technologies, LLMs, multi-modal models, and agentic frameworks for adoption suitabilityStay current with advancements in agentic AI, orchestration frameworks, and production AI engineering practicesDesired Skills and RequirementsMust HaveAgentic AI & Multi-Agent SystemsProduction-grade agentic AI system design and developmentAgentic AI system design & architecture - Multi-agent architectures and orchestration, Agent-to-agent communication, Agent memory and planning strategies, Tool integration and MCP server designAgent orchestration frameworks - LangGraph, Strands Agents, CrewAI, AutoGen, or equivalent agentic AI frameworksLLMOps & AI Platform EngineeringAI Governance & Lifecycle Management - Prompt versioning and governance, evaluation frameworks, regression detectionAI Observability & Monitoring - Output quality monitoring, Agent tracing and observabilityAI Cost Management - Cost governance for high-scale AI workloadsCloud & InfrastructureCloud AI Platforms & Services - AWS cloud ecosystem, AWS Bedrock, AgentCoreCloud-Native Infrastructure & Deployment - Cloud-native AI deployments, Kubernetes, DockerInfrastructure as Code (IaC) - TerraformFoundation Models & AI IntegrationsFoundation model API integration - OpenAI, Anthropic, AWS Bedrock, Azure OpenAI, Hugging FaceMCP and AI tool integration architectureRAG & Knowledge SystemsRetrieval-Augmented Generation (RAG) and Graph-RAG architecturesEmbedding strategies, retrieval and reranking systemsKnowledge graph integrationsTechnical Leadership and CommunicationCross-functional technical collaboration and engineering ownershipTechnical communication and documentationArchitecture reviews and technical decision-makingProactive problem identification and solution developmentGood to HaveDomain...