Job Description

MLOps Engineers will be responsible for deploying, maintaining, monitoring, integrating, and testing machine learning capabilities in production. You will partner with multi-disciplinary teams such as Machine Learning Engineering, Front-End Engineering, Infrastructure Engineering, Data Operations on coordinating delivery of customer-facing features. Your work will contribute to strategic initiatives such as optimization of digital conversion metrics and development of Autodesk Assistant, an LLM-driven chatbot intended to answer customer inquiries.  Our team culture is built on collaboration, mutual support, and continuous learning. 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 as well as our knowledge of trends and techniques relevant to our areas. We encourage personal development and knowledge sharing. 

Responsibilities 

• Model Deployment: Collaborate with data scientists to deploy machine learning models into production environments, ensuring scalability and reliability
• Automation: Develop and maintain CI/CD pipelines to automate the deployment and testing of machine learning models
• Monitoring and Maintenance: Implement monitoring solutions to track model performance and detect anomalies, ensuring models continue to deliver accurate results over time
• Collaboration: Work closely with cross-functional teams, including data scientists, software engineers, and product managers, to integrate machine learning solutions into applications and services
• Security: Ensure the security and compliance of machine learning models and data throughout their lifecycle
• Documentation: Create and maintain comprehensive documentation for model deployment processes, CI/CD pipelines, and infrastructure setups
• Platform Mindset: Partner with internal platform team on the following tasks
• Demonstrate experience with deploying and improving ML features in production
• Demonstrate experience working in cross-functional teams to deliver ML solutions in production
• Ensure best practices in version control, testing, and documentation for ML projects 
• Provide technical leadership and mentorship for less-experienced members of the team

Minimum Qualifications 

Technical Skills: 

• 5 to 8 yrs of experience in MLOps
• Technical Proficiency: Strong understanding of machine learning concepts and familiarity with frameworks (e.g., TensorFlow, PyTorch, Scikit-learn)
• Programming Skills: Proficiency in programming languages such as Python or Java
• DevOps Knowledge: Familiarity with DevOps practices and tools, including Docker, Kubernetes, Jenkins, and Git
• Cloud Computing: Experience with cloud platforms (AWS, Azure) and containerization technologies (Docker, Kubernetes) 
• Pipelines: Proficiency in designing and implementing data pipelines using tools like Apache Airflow, Kubeflow, or MLflow
• Best Practices: Strong understanding of software engineering best practices, including testing, code review, and documentation

Analytical Skills: 

• Strong problem-solving abilities and attention to detail
• Ability to analyze complex datasets and derive actionable insights
• Experience with data visualization tools

Preferred Qualifications

• 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
• Experience with managing infrastructure required for model training, testing, and deployment, including cloud services, databases, and container orchestration platforms
• Experience with distributed computing frameworks like Apache Spark 
• Familiarity with feature stores and experiment tracking tools 
• Knowledge of data governance and ML model governance practices 
• Experience with A/B testing and statistical analysis