Senior MLOps Engineer

Role Overview

Join Guild as a Senior MLOps Engineer to design and implement infrastructure and tooling that enable efficient ML model development, deployment, and iteration. You will drive reliability, scalability, and rapid innovation of Guild’s AI capabilities while collaborating with data scientists, software engineers, and product teams.

Key Missions

• Design, implement, and maintain platforms for deployment, management, and monitoring of ML models and AI agents.
• Develop and optimize CI/CD pipelines tailored for AI and machine learning workflows.
• Partner with data science and engineering teams to streamline ML productionization.
• Provide technical leadership in model governance and mentor others on MLOps standards and best practices.

Responsibilities

• Ensure infrastructure scalability, security, and alignment with best practices.
• Build ML serving and operational capabilities to support production workloads.
• Own/advance governance, versioning, monitoring, and experiment tracking practices for the ML lifecycle.

Requirements

• Strong experience building and maintaining scalable ML infrastructure and pipelines.
• Expertise with Infrastructure as Code (e.g., Terraform) and CI/CD automation (e.g., GitHub Actions, Jenkins).
• Experience with ML deployment/serving frameworks such as TensorFlow Serving, TorchServe, FastAPI, etc.
• Knowledge of monitoring, logging, and alerting for ML models in production.
• Deep understanding of the ML lifecycle: model management, monitoring, version control, and experiment tracking (e.g., MLflow, Kubeflow, Weights & Biases).
• Strong Python coding skills and solid software engineering practices.
• Cloud expertise, particularly AWS, Azure, or GCP, including managed AI/ML services.
• 5–7 years of experience in MLOps, DevOps, software engineering, or related fields.
• Proficiency with containerization and orchestration: Docker and Kubernetes.

Nice to Have

• Experience with MCP (Model Context Protocol), specifically Databricks MCP or AWS MCP.