Principal Engineer - Agentic AI

Autodesk Autodesk · Enterprise · Bangalore, India

Principal Engineer to lead the architecture, development, and enterprise adoption of AI agent platforms, focusing on building intelligent, autonomous systems. This role involves defining technical strategy, establishing best practices, and driving the design and development of scalable multi-agent systems, including orchestration, memory management, tool use, RAG, and evaluation frameworks. The position requires deep expertise in LLMs, cloud-native architectures, and enterprise software engineering, with a strong emphasis on platform capabilities, governance, and operational excellence.

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
  • significant experience building distributed systems and cloud-native platforms
  • 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

  • Principal Engineer
  • Agentic AI Platform & Implementation
  • enterprise adoption
  • next-generation Artificial Intelligence (AI) agent platforms
  • intelligent, autonomous systems
  • technical strategy
  • scalable, secure, and trustworthy AI solutions
  • deep expertise in distributed systems, Large Language Models (LLMs), AI orchestration frameworks, cloud-native architectures, and enterprise software engineering
  • scalable multi-agent systems
  • planning, reasoning, memory management, and tool orchestration
  • engineering standards, architectural patterns, governance models, and best practices
  • technical authority
  • complex AI platform initiatives
  • reusable Agentic AI platform capabilities
  • 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
  • highly available, secure, cloud-native AI services
  • Kubernetes and modern cloud platforms
  • platform observability, resilience, reliability, and operational excellence
  • foundation models
  • AI service providers
  • latency, reliability, quality, scalability, and cost efficiency
  • evaluation frameworks for reasoning quality, hallucination detection, safety, and overall AI system performance
  • reusable Software Development Kits (SDKs), Application Programming Interfaces (APIs), engineering frameworks, and accelerators
  • enterprise standards for AI governance, security, compliance, privacy, and Responsible AI practices
  • Legal, Security, Privacy, and Trust teams
  • secure AI deployment patterns and enterprise guardrails
  • AI platform roadmaps and long-term technical strategy
  • senior technical leaders, Principal Engineers, and engineering teams
  • architectural reviews, technical leadership, and cross-functional collaboration
  • AI engineering best practices
  • emerging AI technologies, frameworks, and research
  • long-term AI strategy
  • advanced autonomous capabilities
  • state-of-the-art reasoning, planning, and orchestration techniques
  • technical conferences, standards organizations, and industry collaborations
  • strategic technology investments and long-term AI platform vision
  • 15+ years of software engineering experience
  • significant experience building distributed systems and cloud-native platforms
  • 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
  • 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
  • foundation models
  • AI frameworks such as LangGraph, LangChain, Semantic Kernel, CrewAI, AutoGen, LlamaIndex, or similar platforms
  • vector databases, Retrieval-Augmented Generation (RAG) architectures, knowledge graphs, and Model Context Protocol (MCP)
  • AI observability, monitoring, and evaluation platforms
  • enterprise-scale platform engineering, identity and access management, Zero Trust security architectures, API management, enterprise integration patterns, DevSecOps, and Machine Learning Operations (MLOps)
  • defining enterprise technology strategy and driving organization-wide engineering transformation

Other signals

  • leading the architecture, development, and enterprise adoption of next-generation Artificial Intelligence (AI) agent platforms
  • define the technical strategy for building intelligent, autonomous systems
  • drive technical direction across multiple engineering organizations
  • establish best practices for agentic AI systems
  • deliver scalable, secure, and trustworthy AI solutions
  • deep expertise in distributed systems, Large Language Models (LLMs), AI orchestration frameworks, cloud-native architectures, and enterprise software engineering
  • design and development of scalable multi-agent systems capable of planning, reasoning, memory management, and tool orchestration
  • Build highly available, secure, cloud-native AI services using Kubernetes and modern cloud platforms
  • Implement evaluation frameworks for reasoning quality, hallucination detection, safety, and overall AI system performance
  • Develop reusable Software Development Kits (SDKs), Application Programming Interfaces (APIs), engineering frameworks, and accelerators for AI product teams
  • Define enterprise standards for AI governance, security, compliance, privacy, and Responsible AI practices
  • Partner with Legal, Security, Privacy, and Trust teams to implement secure AI deployment patterns and enterprise guardrails
  • Evaluate emerging AI technologies, frameworks, and research to inform Autodesk's long-term AI strategy
  • Prototype advanced autonomous capabilities using state-of-the-art reasoning, planning, and orchestration techniques
  • 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