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Abidjan, Abidjan, Côte d’Ivoire
Hybrid
Job Requisition ID #
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 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, optimization, 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 or work experience; equivalent practical/professional experience will also be considered in lieu of a degree.
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 containerization 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 process
Experience with 3D CAD visualization tools in the context of ML models
Experience with enterprise grade security and governance enforcement
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
Learn More
About Autodesk
Welcome to Autodesk! Amazing things are created every day with our software – from the greenest buildings and cleanest cars to the smartest factories and biggest hit movies. We help innovators turn their ideas into reality, transforming not only how things are made, but what can be made.
We take great pride in our culture here at Autodesk – it’s at the core of everything we do. Our culture guides the way we work and treat each other, informs how we connect with customers and partners, and defines how we show up in the world.
When you’re an Autodesker, you can do meaningful work that helps build a better world designed and made for all. Ready to shape the world and your future? Join us!
Salary transparency
Salary is one part of Autodesk’s competitive compensation package. For Canada based roles, we expect a starting base salary between $99,000 and $145,200. Offers are based on the candidate’s experience and geographic location, and may exceed this range. In addition to base salaries, our compensation package may include annual cash bonuses, commissions for sales roles, stock grants, and a comprehensive benefits package.Belonging
We take pride in cultivating a culture of belonging where everyone can thrive. Learn more here: https://www.autodesk.com/company/global-belonging
In-Person Onboarding and Identity Verification
This role may require in-person onboarding and/or in-person ID verification.
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