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 support platform operations.

Responsibilities

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 productive 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, optimisation, 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

Minimum Qualifications

Educational Background: BS or MS in Computer Science, or related field

MLOps Experience: 3+ 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 containerisation 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

Preferred Qualifications

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

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📌 Machine Learning Ops Developer (Ontario)
🏢 Autodesk
📍 Ontario