Principal Engineer - Agentic AI

Autodesk Autodesk · Enterprise · Bangalore, India

Autodesk is seeking a Principal Engineer to lead the architecture, development, and enterprise adoption of AI agent platforms. This role involves defining technical strategy for intelligent autonomous systems, leading the design and development of scalable multi-agent systems, establishing engineering standards, and building highly available, secure, cloud-native AI services. The position requires deep expertise in distributed systems, LLMs, AI orchestration frameworks, and cloud-native architectures, with a focus on platform capabilities like agent orchestration, memory management, RAG, and evaluation frameworks.

What you'd actually do

  1. Define Autodesk's technical vision and reference architecture for enterprise Agentic AI platforms
  2. Lead the design and development of scalable multi-agent systems capable of planning, reasoning, memory management, and tool orchestration
  3. Establish engineering standards, architectural patterns, governance models, and best practices for AI agent development
  4. Serve as the technical authority for complex AI platform initiatives spanning multiple business units
  5. Design reusable Agentic AI platform capabilities, including agent orchestration, memory services, context management, tool execution frameworks, Retrieval-Augmented Generation (RAG), Model Context Protocol (MCP) integration, agent-to-agent communication, and human-in-the-loop workflows

Skills

Required

  • Bachelor's degree in Computer Science, Engineering, or a related field
  • 15+ years of software engineering experience
  • 5+ years of experience designing enterprise-scale Artificial Intelligence (AI), Machine Learning (ML), or intelligent automation platforms
  • Demonstrated experience leading architecture and technical strategy across multiple engineering organizations
  • Strong programming skills in one or more languages such as Python, Java, Go, or C++
  • Deep expertise with cloud platforms, including Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP)
  • Experience with Kubernetes, containers, microservices, and event-driven architectures
  • Expertise with Representational State Transfer (REST) Application Programming Interfaces (APIs), GraphQL, gRPC, messaging systems, and service mesh technologies
  • Exceptional technical leadership, communication, and stakeholder management skills

Nice to have

  • Master's degree or Doctor of Philosophy (PhD) preferred
  • Experience building and deploying production-grade autonomous AI agents
  • Deep understanding of multi-agent architectures, planning and reasoning systems, tool calling, function execution, long-term memory architectures, workflow orchestration, and AI evaluation methodologies
  • Experience with foundation models such as OpenAI, Anthropic, Google Gemini, or equivalent technologies
  • Experience using AI frameworks such as LangGraph, LangChain, Semantic Kernel, CrewAI, AutoGen, LlamaIndex, or similar platforms
  • Experience with vector databases, Retrieval-Augmented Generation (RAG) architectures, knowledge graphs, and Model Context Protocol (MCP)
  • Familiarity with AI observability, monitoring, and evaluation platforms
  • Experience with enterprise-scale platform engineering, identity and access management, Zero Trust security architectures, API management, enterprise integration patterns, DevSecOps, and Machine Learning Operations (MLOps)
  • Experience defining enterprise technology strategy and driving organization-wide engineering transformation

What the JD emphasized

  • enterprise adoption of next-generation Artificial Intelligence (AI) agent platforms
  • define the technical strategy for building intelligent, autonomous systems
  • deep expertise in distributed systems, Large Language Models (LLMs), AI orchestration frameworks, cloud-native architectures
  • building production-grade autonomous AI agents
  • Deep understanding of multi-agent architectures, planning and reasoning systems, tool calling, function execution, long-term memory architectures, workflow orchestration, and AI evaluation methodologies
  • enterprise-scale platform engineering

Other signals

  • leading architecture and development of enterprise AI agent platforms
  • defining technical strategy for intelligent, autonomous systems
  • building scalable multi-agent systems
  • designing reusable Agentic AI platform capabilities
  • implementing evaluation frameworks for AI system performance