Senior ML Engineer: Production-Grade AI Pipelines

Autodesk; Ontario (CA); Sur place; USD 86 000 - 127 000; Plein temps

Résumé du poste

Autodesk is seeking a Machine Learning Engineer to design and operate production-grade AI/ML systems for enterprise-scale use cases. You will build end-to-end pipelines, reusable ML services, and robust evaluation frameworks while focusing on quality, observability, and cost efficiency.

The role requires hands-on experience deploying ML inference pipelines, collaborating with data engineers and analysts, and advancing MLOps practices in a cloud environment.

Qualifications

• Bachelor’s degree in a technical discipline.
• 5+ years building production ML/AI systems.
• Experience designing and operating production ML systems.
• Strong Python programming for production software.
• Experience with Snowflake and cost optimization scenarios.
• Familiar with Airflow or comparable orchestration tools.
• Experience with DBT for data transformation.
• Hands-on ML inference pipeline deployment.

Responsabilités

• Design, develop, test, deploy, and maintain production-grade ML pipelines.
• Develop reusable ML services and components.
• Build and maintain distributed processing pipelines for large data volumes.
• Implement MLOps and LLMOps practices including versioning and monitoring.
• Collaborate with data engineers, data scientists, analysts, and business stakeholders.
• Optimize systems for latency, throughput, and cost.
• Coordinate with platform teams for integration and governance.

Connaissances

Python

Snowflake

Airflow

DBT

MLOps

Cloud AWS

Git

distributed systems

Formation

Bachelor's degree in Computer Science or related

Outils

Snowflake utilities

Snow SQL

Snow Pipe

Airflow

Description du poste

Autodesk is seeking a Machine Learning Engineer to design and operate production-grade AI/ML systems for enterprise-scale use cases. You will build end-to-end pipelines, reusable ML services, and robust evaluation frameworks while focusing on quality, observability, and cost efficiency.

The role requires hands-on experience deploying ML inference pipelines, collaborating with data engineers and analysts, and advancing MLOps practices in a cloud environment.