Role Description Guild is seeking a Senior MLOps Engineer . As a Senior ML Ops Engineer, you'll be pivotal in designing and implementing infrastructure and tooling that allows teams to efficiently develop, deploy, and iterate on machine learning models and AI agents. Your contributions will enable rapid innovation, consistent reliability, and effective scaling of Guild's AI capabilities. This is a role that will be pivotal in establishing Guild’s ML / AI platform. Qualifications - 5–7 years of experience in MLOps, DevOps, software engineering, or related fields. - Strong experience in building and maintaining scalable machine learning infrastructure and pipelines. - Expertise with cloud platforms (AWS, Azure, or GCP), particularly in managed AI/ML services. - Proficiency with containerization (Docker, Kubernetes) and orchestration tools. - Experience in MCP (model context protocol); any specific experience with Databricks MCP or AWS MCP is a plus. - Experience in model deployment frameworks and serving infrastructure (TensorFlow Serving, TorchServe, FastAPI, etc.). - Skilled in infrastructure-as-code tools like Terraform and familiarity with CI/CD automation (GitHub Actions, Jenkins). - Deep understanding of ML lifecycle management, monitoring, version control, and experiment tracking tools (e.g., MLflow, Kubeflow, Weights & Biases). - Strong coding skills, especially in Python, and familiarity with software engineering best practices. - Knowledge of monitoring, logging, and alerting systems for ML models in production. Requirements - Design, implement, and maintain platforms for seamless deployment, management, and monitoring of ML models and AI agents. - Develop and optimize CI/CD pipelines tailored specifically for AI and machine learning workflows. - Collaborate closely with data scientists, software engineers, and product teams to streamline ML model productionization. - Ensure infrastructure is scalable, secure, and adheres to best practices in reliability and observability. - Provide technical leadership in adopting best practices for model governance, versioning, testing, and validation. - Continuously improve platform performance, efficiency, and ease-of-use to accelerate development cycles. - Mentor team members on MLOps standards, practices, and emerging technologies. Benefits - Access to low-cost, high-quality health care options through Collective Health and Kaiser (due to coverage limitations, Kaiser is currently only available in CA & CO). - Access to a 401k to help save for the future. - Vacation policy to rest and recharge. - 8 days of fully-paid sick leave, to take the time to heal and or recover. - Family-friendly benefits, including 12 weeks of parental leave for non-birthing parents and 18-20 weeks for birthing parents; 2-week ramp-up period for when employees return from a leave of 6 weeks or more; as well as employer-paid short-term and long-term disability, employer-sponsored life insurance, fertility and caregiving benefits. - Well-rounded wellness benefits including free and low cost mental health resources and financial wellbeing support services. - Education benefits and tuition assistance to help your future development and growth.

• Create ML models from scratch or improve existing models. • Collaborate with the engineering team, data scientists, and product managers on production models. • Develop experimentation roadmap. • Set up a reproducible experimentation environment and maintain experimentation pipelines. • Monitor and maintain ML models in production to ensure optimal performance. • Write clear and comprehensive documentation for ML models, processes, and pipelines. • Stay updated with the latest developments in ML and AI and propose innovative solutions.

• Create ML models from scratch or improve existing models. • Collaborate with the engineering team, data scientists, and product managers on production models. • Develop experimentation roadmap. • Set up a reproducible experimentation environment and maintain experimentation pipelines. • Monitor and maintain ML models in production to ensure optimal performance. • Write clear and comprehensive documentation for ML models, processes, and pipelines. • Stay updated with the latest developments in ML and AI and propose innovative solutions.

• Ensuring that machine learning models are deployed efficiently and reliably into production environments • Continuously monitoring the performance of models to detect issues like model drift and ensure they remain accurate and effective • Automating the machine learning pipeline, including tasks like data preprocessing, model training, and evaluation • Working closely with data scientists, software engineers, and IT operations to integrate machine learning models into business processes • Managing version control for models and ensuring compliance with governance policies • Identifying and implementing ways to improve the performance and scalability of ML systems • Exploring cloud tools and technologies that assist data science with implementing their usecases.