Principal Machine Learning Software Developer: AI/ML Platform

Overview

MLOps engineer driving AI/ML platform deployment, automation, and operational excellence at Autodesk.

Key Responsibilities

• version control
• deployment automation
• scalable infrastructure
• data pipelines
• monitoring
• security

Tasks

-Version Control and Model Governance: Implement version control systems for machine learning models and contribute to model governance 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 -Solve complex problems of diverse scope by taking a new perspective on existing solutions and applying knowledge of best practices in practical situations. -Collaboration with Data Engineers: Work closely with data engineers to ensure efficient data pipelines for model training and validation -Troubleshooting and Incident Response: Play a key role in identifying and resolving operational issues, contributing to incident response and system recovery -Build effective relationships with more senior practitioners and peers, and build a network of external peers -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 -May begin to act as a mentor or resource for colleagues with less experience -Use data analysis, judgment, and interpretation to select the right course of action -May lead projects or key elements within a broader project -“Connect the dots” of assignments to the bigger picture -May also have accountability for leading and improving on-going processes -Monitoring and Logging: Develop and maintain robust monitoring and logging systems to track model performance, system health, and overall platform efficiency -Operational Efficiency: Drive the operational excellence of our AI/ML Platform by implementing and optimizing MLOps practices -Continuous Improvement: Identify opportunities for process automation, optimization, and implement strategies to enhance the overall MLOps lifecycle

Requirements

• mlops
• tensorflow
• ci/cd
• docker
• kubernetes
• aws

What You Bring

-Analytical advisor role that requires understanding of the theories and concepts of a discipline and the ability to apply best practices -Problem-solving Skills: Proven ability to troubleshoot and resolve complex operational issues in a timely manner -Machine Learning Frameworks: Exposure to popular machine learning frameworks (TensorFlow, PyTorch) and their integration into MLOps processes -Work independently, with close guidance given at critical points -Database Knowledge: Familiarity with databases and data storage solutions commonly used in MLOps, such as SQL, NoSQL, or data lakes -Collaboration Tools: Previous experience with collaboration tools like Git for version control and Jira for project management -Require knowledge and experience such that the incumbent can understand the full range of relevant principles, practices, and practical applications within their discipline -Apply creativity in recommending variations in approach -CI/CD: Demonstrated experience in setting up and managing Continuous Integration and Continuous Deployment (CI/CD) pipelines for machine learning projects -Monitoring Tools: Familiarity with monitoring and logging tools (e.g., Prometheus, Grafana, ELK Stack) for tracking system and model performance -Scripting and Automation: Strong scripting skills in Python, Bash, or similar languages for automating operational processes -Agile Methodology: Familiarity with Agile development methodologies and working in an iterative, collaborative environment -Infrastructure as Code (IaC): Proficiency in implementing Infrastructure as Code practices using tools such as Terraform or Ansible -Security Awareness: Understanding of security best practices in MLOps, including data encryption, access controls, and compliance standards -MLOps Experience: 4+ years of hands-on experience in DevOps and MLOps, with a focus on deploying and managing machine learning models in production environments -Collaboration Skills: Excellent collaboration and communication skills, working effectively with cross-functional teams including data engineers, software developers, and researchers -Cloud Experience: Experience with cloud platforms, especially AWS or Azure, for deploying and managing machine learning infrastructure -Containerization: Strong expertise in containerization technologies (Docker, Kubernetes) for orchestrating and scaling machine learning workloads -Educational Background: BS or MS in Computer Science, or related field

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Benefits

-A common career stabilization point (AKA the “full-contributor” level) for Professional roles

About Autodesk

-Pioneered software for 2D and 3D design, revolutionizing industries. -Known for products like AutoCAD, it reshaped architecture, engineering, and manufacturing workflows. -Empowering creators in fields from construction to digital media, enabling more innovative designs. -Develops tools used in iconic projects, from skyscrapers to blockbuster movies. -Pushes the boundaries of design technology, leading the way in artificial intelligence and automation. -Software is a cornerstone in diverse sectors, from industrial to infrastructure, energy, and entertainment. -Cloud-based solutions streamline design processes and foster real-time collaboration across industries. -A leader in 3D design software, with solutions powering projects in every corner of the globe. -Committed to shaping the future of digital design, bringing complex visions to life.

Sector Specialisms