Position Overview
TheMLOpsEngineering teamis afast-moving teamcombiningsoftware engineeringwith ML system deployment and operations.As anMLOpsEngineer on the team, you willbe responsible forautomating and simplifying machine learning workflows and deployments.MLOpsEngineers work toimplement and deliverMLcapabilitiesto solve complex real-world problems and deliver value toourcustomers.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 willautomateand standardize processes across the ML lifecycle, collaborating with Data Scientists, Software Engineers, andother 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 ourwork andknowledge of trends and techniques relevant to our areas.
Westrive for excellence and pursue it withpersonal developmentandknowledge sharing.
Responsibilities
Designandimplementstreamlined end-to-end, continuous, and automated process of deploying,monitoring,andmaintainingversioned ML models, at scale
Design and implementservices, tests, and interfaces to supportMachine Learning capabilities that improve Autodesk’s eCommerce & Customer platforms
Design andimplement data pipelines and automated workflows to operationalize dataatdifferent stagesof theMachine Learninglifecycle
Collaborate with other members of the team to reach better solutions, and to position our team at thecutting edgeof technology and ML practice
Deploy innovative solutions from non-production environments to production with an eye on scalability and observability
Minimum Qualifications
BS orMS in Computer Science, Statistics, Engineering, Economics, or related field
0-3years of applicable work experienceinSoftware Engineering or Data Engineering orMLEngineering
Proficiencywith the PythonMachine Learningstack,e.g.,Pandas,etc
DemonstrateexpertisewithapplyingMachine Learning, including both Deep Learning (PyTorch) and Classical ML (Scikit-Learn)
Preferred Qualifcations
Familiarity withLarge Language Models, especially in the context of interactive dialog systems and chatbots (RAG, Generative AI, Conversational Agents)
Experience deploying systems that use NLP orexperience working with Conversational AIframeworks
SQL and experience with big data technology such as Hive, Presto, Glue,(Py)Spark, or Athena
Experience with data pipelines andtheAWSML ecosystem
Strong Software Engineering skills