Smartsheet

Manager AI/ML Ops Engineering to lead and mentor a team, define and govern AI/ML operations solutions for scalability, cost-efficiency, and reliability, and develop standardized AI/MLOps workflows including CI/CD/CT pipelines. The role requires experience in enterprise SaaS, building and maintaining AI/ML Ops platform systems, and knowledge of AI/ML frameworks and cloud platforms.

Senior AI/ML Ops Engineer responsible for designing, developing, and overseeing scalable and reliable AI/ML Ops platforms and pipelines. This includes model deployment, CI/CD pipeline development, infrastructure management for training and serving, monitoring, automation of retraining, deployment of foundation models and RAG stacks, resource optimization, and collaboration with data science and engineering teams. The role requires experience with enterprise SaaS, large-scale data, AI/MLOps workflows on platforms like Databricks and MLFlow, cloud platforms, and modern software engineering practices.

Senior AI/ML Ops Engineer responsible for designing, developing, and overseeing scalable and reliable AI/ML Ops platforms and pipelines. This includes model deployment, CI/CD automation, infrastructure management for training and serving, monitoring, resource optimization, and managing foundation models, fine-tuning, and RAG stacks. The role requires close collaboration with data scientists and engineers, and experience with MLOps workflows on Databricks, MLflow, and AI/ML frameworks like LangChain.

This role focuses on engineering and automating the machine learning production lifecycle, including model deployment, retraining, inference, and monitoring. It involves managing cloud infrastructure and ensuring the scalability, reliability, and cost-effectiveness of AI products, acting as a bridge between data scientists and software engineers.

Principal Data Engineer/Architect at Smartsheet focusing on building and optimizing data platforms, pipelines, and infrastructure for AI/ML use cases. The role involves designing scalable data architectures, managing large datasets (Petabytes), and implementing AI/MLOps workflows using tools like Databricks, MLFlow, and LangChain. Emphasis on enterprise SaaS, cloud platforms, and modern software engineering practices.