Principal Network DevOps Engineer

Job Requisition ID # 26WD98484 Position Overview Autodesk is looking for a Principal Network DevOps Engineer to join our Global Network Engineering team. Join us on our journey to provide customers with a world-class Software as a Service (SaaS) platform that powers innovative and industry-changing Autodesk products. We are seeking passionate engineers who are fascinated by cloud networking, automation, and Artificial Intelligence (AI)-driven operations. In this role, you will lead the transformation of traditional network operations into intelligent, autonomous, and continuously improving platforms powered by AI. The ideal candidate combines strong networking and cloud fundamentals with deep expertise in software engineering, Infrastructure as Code (IaC), enterprise integration, and hands-on experience developing Generative AI and Agentic AI systems that deliver measurable operational outcomes. You will design, develop, integrate, and operationalize AI-powered solutions using Large Language Models (LLMs), enterprise knowledge systems, intelligent workflow orchestration, advanced analytics, and automation to modernize network operations and improve engineering productivity. You will collaborate closely with Engineering, Security, Architecture, Cloud, Platform, Operations, and Business teams to build scalable, secure, and resilient platforms while driving enterprise adoption of Agentic AI capabilities. The Global Network Engineering team plays a critical role in designing and deploying stable, consistent infrastructure that meets network engineering standards and supports Autodesk's product objectives. You will contribute to cross-functional initiatives focused on designing, documenting, and implementing secure, automated networking and AI-enabled operational solutions. Responsibilities Design and implement AI-driven operational platforms and intelligent automation solutions for network and cloud infrastructure Develop AI-powered assistants, decision-support systems, and operational intelligence capabilities to improve engineering productivity Design autonomous and semi-autonomous workflows using multi-step reasoning, intelligent orchestration, and automated execution Develop AI-powered troubleshooting, diagnostics, incident triage, Root Cause Analysis (RCA), and operational insight solutions Implement enterprise knowledge retrieval and Retrieval-Augmented Generation (RAG) capabilities that retrieve, correlate, analyze, and act on information from multiple enterprise data sources Develop conversational and self-service experiences using modern collaboration and communication platforms Design human-in-the-loop approval workflows for operational, governance, security, and risk-sensitive activities Implement AI observability, evaluation, monitoring, governance, auditability, and lifecycle management capabilities Establish and promote Responsible AI, security, compliance, and governance standards Evaluate emerging AI technologies and drive the adoption of AI-enabled operational capabilities across the enterprise Participate in the complete network lifecycle, including planning, design, qualification, deployment, operations, and optimization Support the implementation of Autodesk's global network backbone and strategic infrastructure initiatives Define and drive the technical vision, roadmap, and architecture for Autodesk's global network and cloud connectivity platforms Develop, maintain, and enforce network engineering standards to ensure consistent infrastructure deployment Design and develop scalable automation frameworks using Python, Application Programming Interfaces (APIs), Infrastructure as Code tools such as Terraform and AWS CloudFormation, and modern automation technologies Build reusable automation services, integration components, Software Development Kits (SDKs), and orchestration capabilities Implement GitOps and Continuous Integration and Continuous Delivery (CI/CD) workflows across network platforms and automation services Automate network provisioning, validation, compliance assessments, governance controls, and remediation workflows Develop self-service automation platforms that improve efficiency and productivity for Engineering and Operations teams Establish engineering standards, design patterns, and best practices for network automation, Infrastructure as Code, and cloud networking Manage enterprise-scale Secure Access Service Edge (SASE) solutions Design and develop integrations across enterprise systems, cloud platforms, operational tools, security platforms, Information Technology Service Management (ITSM) tools, and knowledge repositories Build scalable connector and API integration frameworks using event-driven and API-first architectures Design secure data exchange and workflow orchestration services across distributed systems Collaborate with Platform and Cloud Engineering teams to deliver scalable, resilient, and secure solutions Build enterprise knowledge platforms that integrate structured and unstructured information, including documentation, runbooks, policies, standards, and operational records Develop knowledge ingestion, enrichment, classification, retrieval, search, summarization, and recommendation capabilities Improve knowledge discoverability and operational decision-making through intelligent knowledge management systems Develop automation solutions that support governance, risk management, compliance, and audit requirements, including intelligent approval workflows and policy enforcement Ensure AI solutions comply with enterprise security, privacy, and regulatory requirements Implement audit trails, traceability, explainability, and operational accountability controls Collaborate with Security and Compliance teams to establish enterprise AI governance standards Develop observability, telemetry, monitoring, reporting, and operational intelligence platforms Create dashboards, alerts, health monitoring, and performance measurement capabilities Implement anomaly detection, predictive analytics, and operational reporting using metrics, logs, traces, events, and AI-driven insights Enable proactive operational management through intelligent analytics and automated insights Minimum Qualifications Bachelor's degree in Computer Science or a related technical field, or equivalent professional experience 7+ years of experience in Software Engineering, Platform Engineering, Automation Engineering, Cloud Engineering, Infrastructure Engineering, Network Engineering, or a related technical discipline Strong expertise in Python development, API development, automation engineering, and software design Hands-on experience with Infrastructure as Code tools such as Terraform or AWS CloudFormation, CI/CD pipelines, GitOps, and enterprise automation platforms Experience designing and implementing large-scale enterprise automation solutions Experience developing integrations across enterprise applications, cloud services, APIs, and operational platforms Strong understanding of cloud platforms, including Amazon Web Services (AWS), Microsoft Azure, or Google Cloud Platform (GCP), with working knowledge of cloud networking and hybrid connectivity Hands-on experience with AWS services such as Transit Gateway, Direct Connect, Lambda, Route 53, Virtual Private Cloud (VPC), and Cloud WAN Knowledge of firewall technologies, network security configurations, and troubleshooting methodologies Strong analytical, troubleshooting, problem-solving, and system design skills Proven experience leading large-scale infrastructure initiatives and influencing technical strategy across multiple teams Experience working effectively within Agile development frameworks Strong presentation skills with the ability to communicate technical policies, proposals, and architectural designs Excellent communication, collaboration, and stakeholder management skills Ability to manage long-term, milestone-driven projects while collaborating across multiple teams Preferred Qualifications Experience designing and implementing Agentic AI solutions and intelligent automation platforms Experience with Generative AI technologies and Large Language Models (LLMs) Experience developing AI-powered assistants, workflow automation solutions, or intelligent operational platforms Understanding of Retrieval-Augmented Generation (RAG), enterprise search, and knowledge integration patterns Experience integrating AI solutions with APIs, operational platforms, monitoring systems, ITSM tools, and enterprise applications Familiarity with AI governance, security, compliance, auditability, explainability, and Responsible AI practices Experience developing conversational experiences and self-service automation platforms Experience building operational analytics, decision-support systems, workflow orchestration, or operational intelligence solutions Experience supporting enterprise-scale AI adoption initiatives Cisco Certified Network Associate (CCNA) certification Cisco Certified Network Professional (CCNP) certification The Ideal Candidate The ideal candidate is a highly experienced and technically accomplished engineering professional who combines deep expertise in network infrastructure, cloud technologies, automation, and Artificial Intelligence with strong architectural judgment and technical leadership to develop secure, scalable, and intelligent platforms that deliver measurable business outcomes Demonstrates deep technical expertise in enterprise networking, cloud infrastructure, automation engineering, and distributed systems Applies strong analytical thinking and sound engineering judgment to solve complex infrastructure and operational challenges Designs scalable, secure, resilient, and maintainable platforms that improve network reliability and operational efficiency Demonstrates a strong understanding of Generative AI and Agentic AI technologies and their potential to transform network operations Takes ownership of comple