Principal Machine Learning Software Developer: AI/ML Platform

Job Requisition ID #

25WD92306

Job Description

Principal Machine Learning Developer - AI/ML Platform

About Autodesk

Autodesk is a global leader in 3D design, engineering, manufacturing, and entertainment software. Our customers use Autodesk software to design and make the physical world that we live in-from complex structures like tall skyscrapers, to strong bridges, to modern cars and even eye-popping movies. The AI/ML Platform helps enable and integrate smart solutions into our software products that improves the design and make process.

Position Overview

Autodesk, a global leader in 3D design, engineering, manufacturing, and entertainment software, is seeking a skilled MLOps Engineer to join our AI/ML Platform team. This role is pivotal in ensuring the smooth operationalization of machine learning models and the overall efficiency of our next-generation AI/ML platform used in the development of machine learning and generative AI solutions powering Autodesk's suite of products and services. You will collaborate with research and product engineering from various domains including design, construction, manufacturing, and media & entertainment to to support platform operations. This role offers a unique opportunity to contribute to the operational success of a strategic AI/ML platform and collaborate with diverse teams to drive innovation in 3D design, engineering, and entertainment software

• Operational Efficiency: Drive the operational excellence of our AI/ML Platform by implementing and optimizing MLOps practices
• Deployment Automation: Design and implement automated deployment pipelines for machine learning models, ensuring seamless transitions from development to production
• Scalable Infrastructure: Collaborate with cross-functional teams to design, implement, and maintain scalable infrastructure for model training, inference, and data processing
• Monitoring and Logging: Develop and maintain robust monitoring and logging systems to track model performance, system health, and overall platform efficiency
• Collaboration with Data Engineers: Work closely with data engineers to ensure efficient data pipelines for model training and validation
• Version Control and Model Governance: Implement version control systems for machine learning models and contribute to model governance practices
• Governance and Trust: Contribute to the implementation of robust model governance practices, version control systems, and adherence to compliance standards. Uphold data privacy and ethical considerations, fostering trust in our AI/ML solutions
• Security and Compliance: Enforce security best practices and compliance standards in all aspects of MLOps, ensuring data privacy and platform security
• Continuous Improvement: Identify opportunities for process automation, optimization, and implement strategies to enhance the overall MLOps lifecycle
• Troubleshooting and Incident Response: Play a key role in identifying and resolving operational issues, contributing to incident response and system recovery

• Educational Background: BS or MS in Computer Science, or related field
• MLOps Experience: 4+ years of hands-on experience in DevOps and MLOps, with a focus on deploying and managing machine learning models in production environments
• Infrastructure as Code (IaC): Proficiency in implementing Infrastructure as Code practices using tools such as Terraform or Ansible
• Containerization: Strong expertise in containerization technologies (Docker, Kubernetes) for orchestrating and scaling machine learning workloads
• CI/CD: Demonstrated experience in setting up and managing Continuous Integration and Continuous Deployment (CI/CD) pipelines for machine learning projects
• Scripting and Automation: Strong scripting skills in Python, Bash, or similar languages for automating operational processes
• Monitoring Tools: Familiarity with monitoring and logging tools (e.g., Prometheus, Grafana, ELK Stack) for tracking system and model performance
• Security Awareness: Understanding of security best practices in MLOps, including data encryption, access controls, and compliance standards
• Collaboration Skills: Excellent collaboration and communication skills, working effectively with cross-functional teams including data engineers, software developers, and researchers
• Problem-solving Skills: Proven ability to troubleshoot and resolve complex operational issues in a timely manner
• Analytical advisor role that requires understanding of the theories and concepts of a discipline and the ability to apply best practices
• A common career stabilization point (AKA the full-contributor level) for Professional roles
• Require knowledge and experience such that the incumbent can understand the full range of relevant principles, practices, and practical applications within their discipline
• Solve complex problems of diverse scope by taking a new perspective on existing solutions and applying knowledge of best practices in practical situations.
• Use data analysis, judgment, and interpretation to select the right course of action
• Apply creativity in recommending variations in approach
• Connect the dots of assignments to the bigger picture
• May lead projects or key elements within a broader project
• May also have accountability for leading and improving on-going processes
• Build effective relationships with more senior practitioners and peers, and build a network of external peers
• Work independently, with close guidance given at critical points
• May begin to act as a mentor or resource for colleagues with less experience

• Cloud Experience: Experience with cloud platforms, especially AWS or Azure, for deploying and managing machine learning infrastructure
• Database Knowledge: Familiarity with databases and data storage solutions commonly used in MLOps, such as SQL, NoSQL, or data lakes
• Machine Learning Frameworks: Exposure to popular machine learning frameworks (TensorFlow, PyTorch) and their integration into MLOps processes
• Collaboration Tools: Previous experience with collaboration tools like Git for version control and Jira for project management
• Agile Methodology: Familiarity with Agile development methodologies and working in an iterative, collaborative environment