Operational Excellence: Drive the operational excellence and technical direction of our AI/ML Platform by implementing and optimizing MLOps practices across the full machine learning development lifecycle
Innovative System Design: Lead the design and engineering of software systems and platform services for the AI/ML Platform, contributing to scalable, secure, and reliable ML development and operations
Deployment Automation: Design and implement automated deployment pipelines for machine learning models and ML artifacts, ensuring seamless transitions from development to production
Workflow Automation: Develop comprehensive systems to automate and optimize laborious ML development and operational processes, integrating them into the platform to streamline operations
Scalable Infrastructure: Collaborate with cross-functional teams to design, implement, and maintain scalable infrastructure for model training, inference, data processing, and ML artifact management
ML Solution Deployment: Develop tools for building, deploying, and operating ML artifacts in production environments, facilitating a smooth transition from development to deployment
Big Data Management: Automate and orchestrate tasks related to managing large-scale data transformation, data processing, and data stores that support model training, validation, deployment, and operations
Scalable Services: Design and implement low-latency, scalable prediction and inference services to support the diverse needs of platform users and Autodesk product teams
Monitoring and Logging: Develop and maintain robust monitoring and logging systems to track model performance, system health, operational reliability, and overall platform efficiency
Collaboration with Data Engineers: Work closely with data engineers to ensure efficient data pipelines for model training, validation, deployment, and ongoing platform operations
Cross-Functional Collaboration: Collaborate across diverse teams, including machine learning researchers, data engineers, software developers, product managers, software architects, and operations teams, fostering a collaborative and cohesive work environment
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 and optimization, and implement strategies to enhance the overall MLOps lifecycle
Architectural Leadership: Take ownership of critical components of the platform, providing architectural direction and contributing to the overall success of the AI/ML Platform