The Role
Define the technical vision for enterprise-scale agentic AI systems and lead architecture, development, reliability, governance, observability, and cost optimization. Design multi-agent platforms, orchestration frameworks, MCP infrastructure, RAG and memory pipelines, and cloud-native deployments. Drive strategic technical initiatives, mentor senior engineers, conduct architecture and code reviews, collaborate across platform and data teams, and evaluate emerging AI technologies. Build prototypes and influence organizational engineering standards, product roadmaps, and technical strategy.
Summary Generated by Built In
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 & Monitoring - Output quality monitoring, Agent tracing and observability
- AI Cost Management - Cost governance for high-scale AI workloads
- Cloud & Infrastructure
- Cloud AI Platforms & Services - AWS cloud ecosystem, AWS Bedrock, AgentCore
- Cloud-Native Infrastructure & Deployment - Cloud-native AI deployments, Kubernetes, Docker
- Infrastructure as Code (IaC) - Terraform
- Foundation Models & AI Integrations
- Foundation model API integration - OpenAI, Anthropic, AWS Bedrock, Azure OpenAI, Hugging Face
- MCP and AI tool integration architecture
- RAG & Knowledge Systems
- Retrieval-Augmented Generation (RAG) and Graph-RAG architectures
- Embedding strategies, retrieval and reranking systems
- Knowledge graph integrations
- Technical Leadership and Communication
- Cross-team technical influence
- Technical communication and documentation
- Organization-level engineering ownership
- Proactive problem identification and resolution
Good to Have
- Domain Experience
- AI systems for cloud operations and infrastructure automation
- Developer tooling platforms
- AI Deployment & Optimization
- Serverless AI deployment patterns
- AI inference cost optimization
- Advanced AI Techniques Exposure
- Model fine-tuning and RLHF
- Advanced model evaluation techniques
- Industry & Community Exposure
- AI-first or cloud-native product company
- Open-source contributions, technical blogs, conference talks, or published research in AI/agentic systems
Experience
- 12+ years of overall software engineering experience
- Prior experience in a Principal Engineer role or equivalent individual contributor (IC) role
- Significant recent hands-on experience building and deploying applied AI systems in production environments
- Proven track record of leading large-scale technical initiatives across multiple teams or product areas
- Demonstrated expertise in architecting enterprise-scale AI platforms and cloud-native AI workloads
- Experience mentoring senior engineers and influencing technical strategy at an organizational level
Education
- Bachelor’s or Master’s degree in Computer Science / Artificial Intelligence / Machine Learning / Engineering / or any related technical discipline
- Equivalent practical experience in advanced AI system design and distributed cloud platforms may also be considered
Skills Required
- 12+ years of overall software engineering experience
- Prior experience in a Principal Engineer role or equivalent individual contributor role
- Significant recent hands-on experience building and deploying applied AI systems in production environments
- Experience leading large-scale technical initiatives across multiple teams or product areas
- Expertise architecting enterprise-scale AI platforms and cloud-native AI workloads
- Experience mentoring senior engineers and influencing technical strategy at an organizational level
- Production-grade agentic AI system design and development
- Experience with multi-agent architectures, orchestration, agent communication, memory, planning, tool integration, and MCP server design
- Experience with agent orchestration frameworks such as LangGraph, Strands Agents, CrewAI, or AutoGen
- AI governance, prompt versioning, lifecycle management, evaluation frameworks, and regression detection
- AI observability, monitoring, output quality monitoring, agent tracing, and observability
- AI cost governance for high-scale workloads
- AWS, AWS Bedrock, and AgentCore experience
- Cloud-native AI deployments using Kubernetes and Docker
- Infrastructure as Code using Terraform
- Foundation model API integration with OpenAI, Anthropic, AWS Bedrock, Azure OpenAI, or Hugging Face
- RAG, Graph-RAG, embeddings, retrieval, reranking, and knowledge graph integrations
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Machine Learning, Engineering, or a related technical discipline
- Experience with AI systems for cloud operations and infrastructure automation
- Experience with developer tooling platforms
- Experience with serverless AI deployment patterns and inference cost optimization
- Exposure to model fine-tuning, RLHF, and advanced model evaluation techniques
- Open-source contributions, technical blogs, conference talks, or published AI research
MontyCloud Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about MontyCloud and has not been reviewed or approved by MontyCloud.
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Fair & Transparent Compensation — Feedback suggests compensation and benefits are viewed favorably overall, indicating competitive pay positioning for many roles.
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Healthcare Strength — Job postings indicate medical, dental, and vision coverage as part of a comprehensive package in the U.S.
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Equity Value & Accessibility — Listings highlight equity participation as a standard component, signaling accessible ownership opportunities for employees.
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The Company
What We Do
MontyCloud is a Seattle, WA based intelligent Cloud Management Platform Company. Our customers use MontyCloud DAY2™ to instantly close the cloud skills gap, simplify CloudOps, and reduce the total cost of cloud operations up to 70%, all in just a few clicks. By leveraging the AWS public cloud, AI, and ML, DAY2 ™ simplifies provisioning, security, compliance, cost optimization, and routine operations. DAY2™’s automation first, No-Code approach helps customers immediately derive deep insights and deliver intelligent Cloud Operations in just a few minutes. You can try the platform for free at https://MontyCloud.com
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