Machine Learning Operations Developer - AI/ML Platform

Autodesk; Vancouver; On-site; CAD 80,000 - 130,000; Full time

Job summary

An established industry player is seeking a skilled MLOps Engineer to enhance their AI/ML Platform. This pivotal role involves optimizing operational efficiency, implementing automated deployment pipelines, and collaborating across various domains to ensure seamless integration of machine learning models. You will be at the forefront of developing innovative solutions that power a suite of products and services, driving advancements in generative AI and machine learning. Join a forward-thinking team where your expertise will contribute to shaping the future of design, construction, and entertainment through cutting-edge technology.

Qualifications

• 5+ years of MLOps experience with a focus on production environments.
• Proficiency in Docker and Kubernetes for machine learning workloads.
• Strong scripting skills in Python or Bash for automation.

Responsibilities

• Drive operational excellence of the AI/ML Platform through MLOps practices.
• Design automated deployment pipelines for machine learning models.
• Collaborate with teams to maintain scalable infrastructure for model training.

Skills

MLOps

DevOps

Infrastructure as Code

Containerization

CI/CD

Scripting

Monitoring and Logging

Security Best Practices

Collaboration Skills

Problem-solving Skills

Education

BS in Computer Science

MS in Computer Science

Tools

Terraform

Ansible

Docker

Kubernetes

Prometheus

Grafana

ELK Stack

AWS

Azure

SQL

Job description

Job Requisition ID #

25WD87571

25WD87571, Machine Learning Operations Developer: AI/ML Platform

About Autodesk
Autodesk makes software for people who make things. We are a global leader in 3D design, engineering, manufacturing, and entertainment software. Our customers use Autodesk software to design and make the physical and virtual worlds that we live in. If you've ever driven a high-performance car, admired a towering skyscraper, used a smartphone, or watched a great film or played an immersive game, chances are you've experienced what millions of Autodesk customers are doing with our software.

Position Overview

Autodesk 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, 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.

• MLOps Experience: 5+ 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 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.