Sr. Machine Learning Operations Engineer

<div class="content-intro"><p>For over 20 years, Smartsheet has helped people and teams achieve–well, anything. From seamless work management to smart, scalable solutions, we’ve always worked with flow. We’re building tools that empower teams to automate the manual, uncover insights, and scale smarter. But more than that, we’re creating space– space to think big, take action, and unlock the kind of work that truly matters. Because when challenge meets purpose, and passion turns into progress, that’s magic at work, and it’s what we show up for everyday.</p></div><p>Smartsheet is hiring a Senior Machine Learning Operations Engineer to architect our machine learning production lifecycle. Your mission is to maintain and deploy ML models to a scalable, reliable, and secure production environment. You will design and maintain the infrastructure, automation, and monitoring systems that ensure our AI products are high-performing and cost-effective.</p> <p>You will report to our Director, Analytics Engineering & Data Governance located in our Bellevue, WA office, or you may work remotely from anywhere in the US where Smartsheet is a registered employer.</p> <p><strong>You Will:</strong></p> <p><strong>Model and Pipeline Automation </strong></p> <ul> <li>Automate the deployment and retraining of ML models, from training through to production inference, by building and managing complete CI/CD/CT (Continuous Training) pipelines, adhering to MLOps best practices. </li> <li>Build, fine-tune, or use pre-trained LLMs, deep learning models or traditional machine learning models.</li> <li>Evaluate and recommend AI or ML solutions for the product using any combination of vendor solutions and/or custom-built models.</li> </ul> <p><strong>Governance & Compliance</strong></p> <ul> <li>Implement model versioning, lineage tracking, and auditing to ensure compliance with security and ethical standards.</li> </ul> <p><strong>Performance Monitoring</strong></p> <ul> <li>Continuously monitor the health and performance of production machine learning models, proactively identifying and correcting model drift, staleness, and performance degradation. </li> <li>Incorporate user feedback for iterative improvements and manage necessary model retraining cycles.</li> </ul> <p><strong>Cross-Functional Collaboration</strong></p> <ul> <li>Act as the "glue" between Data Scientists (who build models) and Software Engineers (who consume them).</li> <li>Partner effectively with software engineers, product managers and business functions to integrate the machine learning solutions across smartsheet.</li> </ul> <p><strong>Architecture and Infrastructure Management</strong></p> <ul> <li>Provision and manage scalable cloud infrastructure using Infrastructure as Code (IaC).</li> <li>Provide architectural guidance and mentorship to a team consisting of ML engineers, data scientists and analytics engineers.</li> <li>Distill complex ML concepts into easy-to-follow technical documentation.</li> </ul> <p><strong>You Have:</strong></p> <ul> <li>5+ years of experience with creating, deploying and scaling machine learning solutions in a cloud environment (eg. AWS, GCP, Azure) and ability to use tools such as SageMaker, Glue, Lambda, Docker etc. to create ML models and data pipelines. </li> <li>7+ years of programming experience in languages used in AI/ML (eg python, scala etc)</li> <li>4+ years of experience in developing deep learning and traditional ML models using common fr ... (truncated, view full listing at source)