Machine Learning Operations Engineer

Position Overview

The ML Ops Engineering team is a fast-moving team combining software engineering with ML system deployment and operations. As an ML Ops Engineer on the team, you will be responsible for automating and simplifying machine learning workflows and deployments. ML Ops Engineers work to implement and deliver ML capabilities to solve complex real-world problems and deliver value to our customers. These processes include model development, testing, integration, release, and infrastructure management.

Our team culture is built on collaboration, mutual support, and continuous learning. In this role, you will automate and standardize processes across the ML lifecycle, collaborating with Data Scientists, Software Engineers, and other cross-functional stakeholders. We emphasize an agile, hands-on, and technical approach at all levels of the team. As a group, we want to continuously improve our work and knowledge of trends and techniques relevant to our areas.

We strive for excellence and pursue it with personal development and knowledge sharing.

Responsibilities

• Design and implement streamlined end-to-end, continuous, and automated process of deploying, monitoring, and maintaining versioned ML models, at scale
• Design and implement services, tests, and interfaces to support Machine Learning capabilities that improve Autodesk’s eCommerce & Customer platforms
• Design and implement data pipelines and automated workflows to operationalize data at different stages of the Machine Learning lifecycle
• Collaborate with other members of the team to reach better solutions, and to position our team at the cutting edge of technology and ML practice
• Deploy innovative solutions from non-production environments to production with an eye on scalability and observability

Minimum Qualifications

• BS or MS in Computer Science, Statistics, Engineering, Economics, or related field
• 0-3 years of applicable work experience in Software Engineering or Data Engineering or ML Engineering
• Proficiency with the Python Machine Learning stack, e.g., Pandas, etc
• Demonstrate expertise with applying Machine Learning, including both Deep Learning (PyTorch) and Classical ML (Scikit-Learn)

Preferred Qualifcations

• Familiarity with Large Language Models, especially in the context of interactive dialog systems and chatbots (RAG, Generative AI, Conversational Agents)
• Experience deploying systems that use NLP or experience working with Conversational AI frameworks
• SQL and experience with big data technology such as Hive, Presto, Glue, (Py)Spark, or Athena
• Experience with data pipelines and the AWS ML ecosystem
• Strong Software Engineering skills