Principal Engineer - Agentic AI
Autodesk · Bengaluru · 15+ yrs experience · Posted 2026-07-21
Tech stack: AWS, Azure, C++, GCP, Go, Google Cloud, GraphQL, Java, Kubernetes, Python
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About the role
Autodesk is seeking a visionary and hands-on Principal Engineer – Agentic AI Platform & Implementation to lead the architecture, development, and enterprise adoption of next-generation Artificial Intelligence (AI) agent platforms. This role will define the technical strategy for building intelligent, autonomous systems that enhance Autodesk products, internal engineering workflows, and business operations.
As a Principal Engineer, you will drive technical direction across multiple engineering organizations, establish best practices for agentic AI systems, and collaborate closely with Product Management, Research, Security, Infrastructure, and executive leadership to deliver scalable, secure, and trustworthy AI solutions.
This position requires deep expertise in distributed systems, Large Language Models (LLMs), AI orchestration frameworks, cloud-native architectures, and enterprise software engineering.
Roles and Responsibilities
Define Autodesk's technical vision and reference architecture for enterprise Agentic AI platforms
Lead the design and development of scalable multi-agent systems capable of planning, reasoning, memory management, and tool orchestration
Establish engineering standards, architectural patterns, governance models, and best practices for AI agent development
Serve as the technical authority for complex AI platform initiatives spanning multiple business units
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
Build highly available, secure, cloud-native AI services using Kubernetes and modern cloud platforms
Drive platform observability, resilience, reliability, and operational excellence
Architect integrations with leading foundation models and AI service providers
Optimize AI agent workflows for latency, reliability, quality, scalability, and cost efficiency
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
Lead Product Management partnerships to define AI platform roadmaps and long-term technical strategy
Mentor senior technical leaders, Principal Engineers, and engineering teams across Autodesk
Influence engineering strategy through architectural reviews, technical leadership, and cross-functional collaboration
Drive adoption of AI engineering best practices through documentation, technical workshops, and engineering communities
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
Represent Autodesk at technical conferences, standards organizations, and industry collaborations
Contribute to strategic technology investments and long-term AI platform vision
Responsibilities:
- Define Autodesk's technical vision and reference architecture for enterprise Agentic AI platforms
- Lead the design and development of scalable multi-agent systems capable of planning, reasoning, memory management, and tool orchestration
- Establish engineering standards, architectural patterns, governance models, and best practices for AI agent development
- Serve as the technical authority for complex AI platform initiatives spanning multiple business units
- 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
- Build highly available, secure, cloud-native AI services using Kubernetes and modern cloud platforms
- Drive platform observability, resilience, reliability, and operational excellence
- Architect integrations with leading foundation models and AI service providers
- Optimize AI agent workflows for latency, reliability, quality, scalability, and cost efficiency
- 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
- Lead Product Management partnerships to define AI platform roadmaps and long-term technical strategy
- Mentor senior technical leaders, Principal Engineers, and engineering teams across Autodesk
- Influence engineering strategy through architectural reviews, technical leadership, and cross-functional collaboration
- Drive adoption of AI engineering best practices through documentation, technical workshops, and engineering communities
- 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
- Represent Autodesk at technical conferences, standards organizations, and industry collaborations
- Contribute to strategic technology investments and long-term AI platform vision
Qualifications:
- Bachelor's degree in Computer Science, Engineering, or a related field, with a Master's degree or Doctor of Philosophy (PhD) preferred
- 15+ years of software engineering experience, including 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
- 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
- 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
Qualifications
- Bachelor's degree in Computer Science, Engineering, or a related field, with a Master's degree or Doctor of Philosophy (PhD) preferred
- 15+ years of software engineering experience, including 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
- 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
Responsibilities
- Define Autodesk's technical vision and reference architecture for enterprise Agentic AI platforms
- Lead the design and development of scalable multi-agent systems capable of planning, reasoning, memory management, and tool orchestration
- Establish engineering standards, architectural patterns, governance models, and best practices for AI agent development
- Serve as the technical authority for complex AI platform initiatives spanning multiple business units
- 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
- Build highly available, secure, cloud-native AI services using Kubernetes and modern cloud platforms
- Drive platform observability, resilience, reliability, and operational excellence
- Architect integrations with leading foundation models and AI service providers
- Optimize AI agent workflows for latency, reliability, quality, scalability, and cost efficiency
- 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
- Lead Product Management partnerships to define AI platform roadmaps and long-term technical strategy
- Mentor senior technical leaders, Principal Engineers, and engineering teams across Autodesk
- Influence engineering strategy through architectural reviews, technical leadership, and cross-functional collaboration
- Drive adoption of AI engineering best practices through documentation, technical workshops, and engineering communities
- 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
- Represent Autodesk at technical conferences, standards organizations, and industry collaborations
- Contribute to strategic technology investments and long-term AI platform vision