Principal Machine Learning Developer: AI/ML Platform

Job Requisition ID # 26WD99908 Principal Machine Learning Operations Developer: AI/ML Platform Location: Canada. Open to Toronto, Ontario or Remote Ontario. About Autodesk Autodesk makes software for people who make things. We are a global leader in 3D design, engineering, manufacturing, and entertainment software. Our customers use Autodesk software to design and make the physical and virtual worlds that we live in. If you've ever driven a high-performance car, admired a towering skyscraper, used a smartphone, or watched a great film or played an immersive game, chances are you've experienced what millions of Autodesk customers are doing with our software. Position Overview Autodesk, a global leader in 3D design, engineering, manufacturing, and entertainment software, is seeking a skilled Principal MLOps Developer 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 deliver platform capabilities that support the full AI/ML development lifecycle. As a principal-level contributor, you will help build innovative capabilities that enable faster, more secure development and deployment of machine learning and generative AI solutions. You will take ownership of critical platform components, provide architectural direction, and contribute to scalable systems for model training, inference, data processing, deployment automation, monitoring, governance, and operations. Responsibilities Operational Excellence: Drive the operational excellence and technical direction of our AI/ML Platform by implementing and optimizing MLOps practices across the full machine learning development lifecycle Innovative System Design: Lead the design and engineering of software systems and platform services for the AI/ML Platform, contributing to scalable, secure, and reliable ML development and operations Deployment Automation: Design and implement automated deployment pipelines for machine learning models and ML artifacts, ensuring seamless transitions from development to production Workflow Automation: Develop comprehensive systems to automate and optimize laborious ML development and operational processes, integrating them into the platform to streamline operations Scalable Infrastructure: Collaborate with cross-functional teams to design, implement, and maintain scalable infrastructure for model training, inference, data processing, and ML artifact management ML Solution Deployment: Develop tools for building, deploying, and operating ML artifacts in production environments, facilitating a smooth transition from development to deployment Big Data Management: Automate and orchestrate tasks related to managing large-scale data transformation, data processing, and data stores that support model training, validation, deployment, and operations Scalable Services: Design and implement low-latency, scalable prediction and inference services to support the diverse needs of platform users and Autodesk product teams Monitoring and Logging: Develop and maintain robust monitoring and logging systems to track model performance, system health, operational reliability, and overall platform efficiency Collaboration with Data Engineers: Work closely with data engineers to ensure efficient data pipelines for model training, validation, deployment, and ongoing platform operations Cross-Functional Collaboration: Collaborate across diverse teams, including machine learning researchers, data engineers, software developers, product managers, software architects, and operations teams, fostering a collaborative and cohesive work environment 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 and optimization, and implement strategies to enhance the overall MLOps lifecycle Architectural Leadership: Take ownership of critical components of the platform, providing architectural direction and contributing to the overall success of the AI/ML Platform Basic Qualifications   Educational Background: BS or MS in Computer Science, or equivalent practical experience Experience: 8 + years of experience in software development and engineering, with a solid record of delivering production systems and services Strong background in AI/ML with experience in deep learning, statistical modeling, and neural networks Expertise in AI/ML Technologies : Hands-on experience with AI/ML frameworks (such as TensorFlow , PyTorch ) and familiarity with the lifecycle of AI/ML model development, from training to deployment Proficiency in Programming Languages : Strong coding skills in languages commonly used in AI/ML and system development, such as Python, Java, or Go  Strong Analytical and Problem-Solving Skills : Ability to tackle complex technical challenges, analyze potential solutions, and implement the most effective ones Excellent Communication and Teamwork Abilities : Strong communication skills to effectively collaborate with cross-functional teams, along with the ability to work independently System Performance Optimization: Deep understanding of performance met