Machine Learning Ops Developer

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 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, 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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