Senior Machine Learning Operations Developer: AI/ML Platform

Das ist der Job

Location: Canada.

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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. You will collaborate with research and product engineering teams across design, construction, manufacturing, and media & entertainment to support platform operations.

Scalable Infrastructure: Collaborate with cross‑functional teams to design, implement, and maintain scalable infrastructure for model training, inference, and data processing. Collaboration Skills: Excellent collaboration and communication skills, working effectively with cross‑functional teams.

In addition to base salaries, our compensation package may include annual cash bonuses, commissions for sales roles, stock grants, and a comprehensive benefits package. #J-18808-Ljbffr Open to Toronto, Montreal, Vancouver or Remote Canada.

This role ensures the smooth operationalization of machine learning models and the overall efficiency of our next‑generation AI/ML platform that powers Autodesk’s suite of products and services. 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. 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 robust model governance practices, compliance standards, data privacy, and ethical considerations. Security and Compliance: Enforce security best practices and compliance standards to ensure 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: Identify and resolve operational issues, contributing to incident response and system recovery.

Minimum Qualifications Educational Background: BS or MS in Computer Science or related field. MLOps Experience: 5+ years of hands‑on experience in DevOps and MLOps, focusing on deploying and managing machine learning models in production environments. Infrastructure as Code: Proficiency in implementing IaC practices using Terraform or Ansible.

Containerization: Strong expertise in Docker and Kubernetes for orchestrating and scaling machine learning workloads. CI/CD: Experience setting up and managing CI/CD pipelines for machine learning projects. Scripting and Automation: Strong scripting skills in Python or Bash for automating operational processes.

Monitoring Tools: Familiarity with Prometheus, Grafana, ELK Stack for tracking system and model performance. Security Awareness: Understanding of security best practices in MLOps, including encryption, access controls, and compliance. Problem‑Solving Skills: Proven ability to troubleshoot and resolve complex operational issues promptly.

Preferred Qualifications Cloud Experience: Experience deploying and managing machine learning infrastructure on AWS or Azure. Database Knowledge: Familiarity with SQL, NoSQL, or data lake solutions commonly used in MLOps. Machine Learning Frameworks: Exposure to TensorFlow or PyTorch and integration into MLOps processes.

Collaboration Tools: Experience with Git for version control and Jira for project management. Agile Methodology: Familiarity with Agile development methodologies. Salary Transparency For Canada‑based roles, we expect a starting base salary between $0 and $0.

Offers are based on the candidate’s experience and geographic location, and may exceed this range.