ML Ops Developer, Fusion

Autodesk; Toronto; Hybrid; CAD 80,000 - 100,000; Full time

Job summary

A leading design software company is seeking an MLOps Developer to optimize AI-powered applications and improve operational efficiency. The role involves collaborating with engineers on deployment pipelines and ensuring reliability for ML applications. Candidates should have 3–5 years of MLOps experience, expertise in cloud services like AWS/Azure, and familiarity with Docker and Terraform. This position offers a hybrid work model across Canada.

Qualifications

• 3–5 years of hands-on experience in MLOps / DevOps in a production environment.
• Experience with cloud platforms like AWS or Azure.
• Proficiency in Infrastructure as Code practices.

Responsibilities

• Improve operational efficiency through MLOps practices.
• Develop and maintain model inference services.
• Adapt research models to automated deployment pipelines.

Skills

MLOps / DevOps Experience

Cloud Experience

Infrastructure as Code (IaC)

Containerization

CI/CD Experience

Inference Services

Security Awareness

Tools

Docker

Kubernetes

Terraform

AWS

Azure

Flask

FastAPI

Job description

Overview

The Fusion Machine Learning team is a multi‑disciplinary group of engineers and researchers developing AI/ML solutions to some of the biggest problems in 3D design, manufacturing, and mechanical engineering. We are seeking an MLOps Developer who is passionate about transforming mature production‑ready AI‑powered applications through optimizing the underlying infrastructure.

You will collaborate with researchers and engineers to continuously improve data, train and release pipelines by automating repositories to ensure quality and interoperability with deployment systems, and upgrade prototype code to run on large cloud‑based ML training infrastructure.

You will report to the Fusion Platform ML manager and play a critical role in the Autodesk AI strategy. The team is hybrid‑remote, located across Canada and the US.

Responsibilities

• Operational Efficiency: Improve the team’s efficiency by implementing MLOps and DevOps practices.
• Model Inference: Develop, maintain, and ensure the reliability of the team’s model inference services.
• Deployment Automation: Adapt research models to automated deployment pipelines.
• Scalable Development: Collaborate with engineers and researchers to ensure research code can transition to training and inference at scale.
• Monitoring and Logging: Track model performance, system health, and overall platform efficiency.
• Version Control and Model Governance: Mature single‑version codebases to iterable, pipeline‑deployed and version‑controlled solutions.
• Security, Compliance and Governance: Contribute to the implementation of model governance practices and enforce company‑wide security best practices and compliance standards in all aspects of DevOps and MLOps.
• Continuous Improvement: Identify opportunities for process automation, optimization, and implement strategies to enhance the MLOps lifecycle.
• Troubleshooting and Incident Response: Collaborate with cross‑functional teams to ensure SLA and requirements for new features and releases are met.

Minimum Qualifications

• MLOps / DevOps Experience: 3–5 years of hands‑on experience in MLOps / DevOps in a production environment.
• Cloud Experience: Experience with cloud platforms, such as AWS or Azure, for deploying and managing machine learning infrastructure (SageMaker Endpoints, AzureML).
• Infrastructure as Code (IaC): Proficiency in implementing Infrastructure as Code practices using tools such as Terraform.
• Containerization: Expertise in containerization technologies (Docker, Kubernetes) for orchestrating and scaling machine learning applications.
• CI/CD: Demonstrated experience setting up and managing Continuous Integration and Continuous Deployment (CI/CD) pipelines for machine learning projects.
• Inference Services: Experience in building reliable and scalable inference APIs (Flask, FastAPI).
• Security Awareness: Understanding of security best practices in MLOps, including data encryption, access controls, and compliance standards.

Preferred Qualifications

• Machine Learning Frameworks: Exposure to popular machine learning frameworks (TensorFlow, PyTorch) and their integration into MLOps processes.
• Inference Acceleration: Familiarity with leveraging inference accelerator tools (ONNX, TensorRT, Triton) for real‑time and high‑throughput inference runtimes.
• Monitoring Tools: Familiarity/experience with monitoring and logging tools (Prometheus, Grafana, ELK stack) for tracking model and system performance.

About Autodesk

Welcome to Autodesk! We help innovators turn their ideas into reality, transforming not only how things are made, but what can be made. Join us to work on meaningful projects that build a better world.