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) certificati