Principal Core Services Engineer(AI/ML Focus)

Position Overview

The Principal Core Services Engineer(AI/ML Focus) within Autodesk’s Cloud Infrastructure, Platform & Engineering (CIPE) organization will be a key contributor in advancing our next-generation intelligent cloud platform and Developer tools. This role focuses on building scalable, secure, and self-optimizing systems that power Autodesk’s global engineering ecosystem and Tier-0 services.

You will design and develop AI-enabled, cloud-native solutions leveraging AWS, Kubernetes, and Python, integrating Agentic AI workflows to automate complex operations, improve reliability, and drive platform-level resilience. Working across hybrid and multi-cloud environments, you will help standardize architectures and accelerate Autodesk’s transformation toward autonomous infrastructure services.

The ideal candidate combines strong software engineering fundamentals with deep cloud infrastructure knowledge, and brings a mindset of innovation, reliability, and operational rigor. You will collaborate with architects, service owners, and AI platform teams to deliver production-ready systems that enhance developer productivity, service uptime, and global Service scalability.

Responsibilities

• Design, optimize, and maintain Core Services, with a strong focus on DNS, foundational cloud infrastructure, and service reliability across hybrid and multi-cloud environments
• Build and enhance cloud-native services on AWS and Azure, leveraging Python for scalable, performant, and secure system design
• Develop and deploy containerized workloads using Kubernetes, ensuring efficiency, resilience, and operational consistency
• Implement Agentic AI capabilities to drive intelligent automation, proactive issue resolution, and continuous performance optimization
• Automate infrastructure provisioning, CI/CD pipelines, observability, and monitoring to strengthen platform efficiency and reduce operational overhead
• Collaborate with architecture and engineering teams to define, evolve, and standardize core service patterns and foundational designs
• Design and implement AI-driven agents leveraging MCP (Model Context Protocol) for tool interoperability, dynamic orchestration, and autonomous task execution across platforms
• Develop adaptive workflows and intelligent orchestration frameworks that enable DNS and other foundational services to self-heal, scale intelligently, and operate with minimal manual intervention
• Troubleshoot and resolve complex distributed system challenges, with emphasis on performance tuning, network optimization, and DNS service reliability
• Contribute to engineering best practices, design reviews, and architectural evolution to enhance system efficiency and maintainability

Required Qualifications

• Bachelor’s or Master’s degree in Computer Science, Engineering, or equivalent professional experience
• Proven expertise in Core Services engineering (DNS, networking, service reliability) and foundational service optimization
• Strong hands-on experience with AWS services (e.g., EC2, EKS, Lambda, S3, RDS, IAM) and exposure to Azure environments
• Proficiency in Kubernetes orchestration and containerization for large-scale, production-grade systems
• Advanced Python development skills for backend, automation, and service optimization
• Hands-on experience with MCP (Model Context Protocol) or equivalent frameworks.
• Deep understanding of LLMs, AI agent architectures, and orchestration frameworks (e.g., LangChain, AutoGen, Semantic Kernel)
• Familiarity with Infrastructure-as-Code tools (Terraform, CloudFormation, AWS CDK)
• Experience with CI/CD pipelines, DevOps practices, and cloud networking fundamentals
• Solid understanding of networking, DNS architecture, cloud security, and distributed system design

Preferred Qualifications

• Experience designing or managing Core DNS Services, authoritative and recursive DNS, and traffic management across multi-region environments
• Background in intelligent agent development, AI-driven orchestration, or self-optimizing infrastructure systems
• Exposure to multi-cloud and hybrid architectures, including performance and cost optimization across providers
• Familiarity with observability stacks such as Prometheus, Grafana, ELK, CloudWatch, or equivalent
• Contributions to open-source projects in DNS, AI, Kubernetes, or cloud-native ecosystems
• Strong analytical thinking, cross-functional collaboration, and communication skills

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