MLOps Engineer

Coppell

We are seeking an **MLOps Engineer** who can bridge the gap between AI model development and production deployment. This role is ideal for someone who understands how AI and Computer Vision models are built, evaluated, and deployed, while also possessing the infrastructure expertise needed to operate them reliably at scale. The ideal candidate has hands-on experience taking machine learning models from experimentation to production and supporting them in real-world environments. This is not a traditional DevOps position. We are looking for someone who understands AI workloads and can partner effectively with data scientists, machine learning engineers, and software development teams. **Key Responsibilities:** Model Deployment \& Production Operations * Own the end-to-end process of moving AI and Computer Vision models from experimentation into production environments. * Deploy, monitor, troubleshoot, and optimize machine learning workloads in production. * Ensure deployments are scalable, reliable, secure, and cost effective. * Support ongoing model operations, incident response, and production performance management. MLOps Platform \& Infrastructure * Design, build, and maintain MLOps infrastructure and deployment pipelines. * Develop automation for model packaging, deployment, testing, and lifecycle management. * Implement model versioning, experiment tracking, and reproducibility practices. * Manage GPU-enabled infrastructure and cloud resources used for training and inference. Containerization \& Cloud Engineering * Build and manage containerized workloads using Docker. * Deploy and orchestrate applications using Kubernetes. * Optimize cloud-based AI infrastructure within Azure environments. * Support infrastructure-as-code and automation initiatives. CI/CD \& Monitoring * Create and maintain CI/CD pipelines for machine learning workloads. * Implement monitoring, alerting, observability, and model performance tracking. * Develop dashboards and reporting to monitor system health, model accuracy, and operational metrics. * Establish processes for drift detection and ongoing model validation. Cross-Functional Collaboration * Partner closely with data scientists and machine learning engineers to streamline model deployment and production readiness. * Provide technical guidance on deployment strategies, scalability, monitoring, and operational best practices. * Act as a technical bridge between AI development and platform engineering teams. **Required Qualifications:** Experience * 3 years of experience in MLOps, ML Platform Engineering, AI Infrastructure Engineering, or a related field. * Proven experience deploying machine learning or Computer Vision models into production environments. * Demonstrated experience supporting AI workloads after deployment, including monitoring, troubleshooting, and optimization. Technical Skills * Strong experience with Azure cloud services and AI-related infrastructure. * Hands-on experience with Kubernetes and Docker. * Experience designing and maintaining CI/CD pipelines. * Experience with MLflow or similar tools for experiment tracking and model versioning. * Strong Python programming and automation skills. * Experience implementing production monitoring, observability, and alerting solutions. * Knowledge of GPU-based compute environments for AI training and inference workloads. AI / Machine Learning Knowledge * Understanding of machine learning lifecycle concepts, including training, evaluation, deployment, and monitoring. * Familiarity with Computer Vision models and production AI systems. * Ability to work effectively with machine learning engineers and data scientists on deployment strategies. Top Must-Haves * Hands-on experience deploying AI or Computer Vision models into production. * Strong MLOps background including Kubernetes, Docker, CI/CD, MLflow, monitoring, and model versioning. * Azure cloud experience supporting AI infrastructure and GPU-enabled workloads.