Principal Engineer - AI Engineering

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Principal Engineer - AI Engineering

MontyCloud

Bengaluru

12+ years

Today

$92.8K–101.2K/yr

Full-time

Onsite

Skills Required

Agentic AI

LLMOps

OpenAI

Anthropic

Azure OpenAI

Hugging Face

RAG

Graph-RAG

Embeddings

Retrieval

Prompt Engineering

Fine-tuning

Multi-Agent Systems

LangGraph

Strands Agents

Description

Principal Engineer - AI Engineering at MontyCloud. This role defines and drives the technical vision for agentic AI systems powering intelligent cloud operations.

Company: MontyCloud

Role: Principal Engineer - AI Engineering

Location: MontyCloud Bangalore Office

Experience

• 10 - 14
• 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
• 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
  
  

Qualification

• Bachelor’s degree in Computer Science, Artificial Intelligence, Machine Learning, Engineering, or a related technical discipline
• 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
  
  

Responsibilities

• 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 and technical standards
• Design and develop critical AI platform components and infrastructure
• Establish AI engineering best practices across the organisation, including 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
• 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
• 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
  
  

Additional Responsibilities

• 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
• Contribute to MontyCloud’s technical brand through technical writing, open-source contributions, or speaking engagements
• Lead strategic technical initiatives across the organization
  
  

Nice To Have

• AI systems for cloud operations and infrastructure automation
• Developer tooling platforms
• Serverless AI deployment patterns
• AI inference cost optimization
• Model fine-tuning and RLHF
• Advanced model evaluation techniques
• AI-first or cloud-native product company experience
• Open-source contributions, technical blogs, conference talks, or published research in AI/agentic systems
  
  

More Skills

Multi-agent architectures, Orchestration frameworks, Agent-to-agent communication, Agent memory, Planning strategies, Tool integration, MCP server design, CrewAI, AutoGen, Prompt versioning, Governance, Evaluation frameworks, Regression detection, AI observability, Agent tracing, Cost governance, AWS cloud ecosystem, AWS Bedrock, AgentCore, Cloud-native AI deployments, Kubernetes, Docker, Terraform, Embedding strategies, Retrieval systems, Reranking systems, Knowledge graphs, Technical communication, Documentation

Prepare for this role

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Top 10 RAG Quick-Prep Questions

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Top 25 LLMOps Interview Questions (Extended)

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