Senior Machine Learning Operations Engineer - Inference, AI​/ML Platform

Job

Requisition  # 26WD94525

The French translation can be found below!/La traduction en français se trouve plus bas!

Position Overview
Autodesk, a global leader in 3D design, engineering, manufacturing, and entertainment software, is seeking a skilled

Senior MLOpsDeveloper 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 ournext-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 totosupport platform operations.

Responsibilities

Operational Efficiency:

Drive the operational excellence of our AI/ML Platform by implementing andoptimizingMLOpspractices

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, andmaintainscalable infrastructure for model training, inference, and data processing

Monitoring and Logging:

Develop andmaintainrobust monitoring and logging systems to track model performance, system health, and overall platform efficiency

Collaboration with Data Developers:

Work closely with data developers 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 ofMLOps, ensuring data privacy and platform security

Continuous Improvement:

Identify opportunities for process automation, optimization, and implement strategies to enhance the overallMLOpslifecycle

Troubleshooting and Incident Response:

Play a key role inidentifyingand 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:

5+ years of hands-on experience in Dev Ops andMLOps, 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:

Strongexpertisein 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 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 inMLOps, including data encryption, access controls, and compliance standards

Collaboration

Skills:

Excellent collaboration and communication skills, working effectively with cross-functional teams including data developers, software developers, and researchers

Problem-solving

Skills:

Proven ability to troubleshoot and resolve complex operational issuesin 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 inMLOps, such as SQL, No

SQL, or data lakes

Machine Learning Frameworks:

Exposure to popular machine learning frameworks (Tensor Flow,PyTorch) and their integration intoMLOpsprocesses

Collaboration…