MLOps Engineer, Intapp

• Bachelor’s degree in computer science, Engineering, Statistics, or a related field
• Experience in software development or data engineering, with at least 3 years focused on MLOps or similar roles
• Proven track record in designing and deploying scalable machine learning systems in production
• Strong programming skills in Python and experience with ML frameworks and tools (e.g., TensorFlow, PyTorch, MLFlow, MetaFlow, vLLM, Kubeflow, Jupyter notebook, Azure ML Studio, Amazon Sagemaker, Apache Spark, Apache Flink)
• Expertise in containerization technologies (e.g., Docker, Kubernetes) and automation tools (e.g., Jenkins, GitLab CI)
• Excellent problem-solving skills and the ability to work independently or as part of a team

Desirable

• As a MLOps Engineer, you will play a crucial role in enabling applied AI
• Your main focus will be on the design, build, and maintenance of secure, scalable and efficient ML Platform, with a platform as a product mindset, that automates the end-to-end life-cycle for traditional ML models and LLM models, as part of the Cloud platforms engineering (CPE) directorate. CPE’s mission is to enable our Engineering teams to ship value faster, securely, efficiently and reliably
• Design and implement robust MLOps and LLMOps pipelines to automate and optimize machine learning model training, testing, deployment, and scaling
• Collaborate with data scientists and software engineers to ensure operational criteria are met before deployment
• Maintain and enhance continuous integration (CI) and continuous deployment (CD) environments for machine learning systems
• Develop tools to improve visibility into the system's operation and to facilitate rapid troubleshooting and debugging
• Foster a culture of continuous improvement by incorporating feedback and lessons learned into future ML deployments
• Lead initiatives to increase the resilience and scalability of ML systems