Senior MLOps Engineer — Coppell, TX

We are seeking a hands-on Senior MLOps Engineer to help scale and operationalize AI and machine learning solutions across the organization. This role will focus on deploying, managing, and supporting machine learning workloads in production environments while building the infrastructure, automation, and monitoring capabilities required for long-term success.

The ideal candidate has direct experience moving machine learning models from development into production and supporting those solutions throughout their lifecycle. This individual should be comfortable working alongside Data Scientists and Machine Learning Engineers to ensure AI systems are reliable, scalable, and maintainable in real-world environments.

This is not a traditional DevOps role. We are specifically seeking someone who has worked with production AI/ML workloads and understands the challenges associated with model deployment, inference serving, GPU infrastructure, monitoring, and operational support.

Key Responsibilities:

AI/ML Deployment & Production Support

• Deploy machine learning models and AI applications into production environments.

• Manage and support live AI/ML workloads, including performance monitoring, troubleshooting, and optimization.

• Ensure AI services meet availability, scalability, security, and reliability requirements.

• Support production inference environments and optimize resource utilization.

• Maintain operational standards and processes for AI systems running at scale.

• Participate in production incident response and ongoing platform support activities.

MLOps & Automation

• Design and maintain deployment workflows for machine learning solutions.

• Automate model packaging, testing, deployment, and release processes.

• Establish model versioning, experiment tracking, and lifecycle management practices.

• Support end-to-end machine learning pipelines from development through production operations.

• Improve deployment consistency, repeatability, and operational efficiency across AI initiatives.

Cloud & Infrastructure Engineering

• Build and support cloud infrastructure used for AI and machine learning workloads.

• Deploy and manage containerized applications using Docker and Kubernetes.

• Optimize Azure-based environments supporting model training and inference.

• Implement Infrastructure-as-Code and automation best practices.

• Ensure AI platforms remain scalable, resilient, and cost-efficient.

GPU Infrastructure & Model Serving

• Manage GPU-enabled environments supporting machine learning inference workloads.

• Support model serving platforms and optimize performance for production deployments.

• Monitor GPU utilization, throughput, latency, and resource consumption.

• Scale inference environments to support evolving business requirements.

• Help establish best practices for production AI serving architectures.

Monitoring & Reliability

• Develop and maintain CI/CD pipelines supporting AI and machine learning workloads.

• Implement monitoring, alerting, logging, and observability solutions.

• Build dashboards and reporting capabilities for infrastructure and model performance.

• Support model validation, drift detection, and production health monitoring.

• Continuously improve deployment reliability and operational performance.

Collaboration

• Work closely with Data Scientists, Machine Learning Engineers, and Software Engineers to productionize AI solutions.

• Provide guidance on deployment architecture, scalability, monitoring, and operational readiness.

• Serve as a key contributor in bridging AI development and infrastructure operations.

Required Qualifications:

Experience

• 5+ years of experience in MLOps, Machine Learning Engineering, AI Infrastructure Engineering, ML Platform Engineering, or a related field.

• Hands-on experience deploying machine learning models into production environments.

• Experience operating and supporting AI/ML solutions after deployment.

• Proven experience running end-to-end AI/ML pipelines in production.

• Experience supporting cloud-based machine learning platforms and services.

• Experience with production inference environments and model serving architectures.

Technical Skills

• Strong Azure cloud experience supporting AI and machine learning workloads.

• Hands-on experience with Kubernetes and Docker.

• Experience designing and supporting CI/CD pipelines.

• Strong Python scripting and automation capabilities.

• Experience with MLflow or similar solutions for model lifecycle management.

• Experience implementing monitoring, alerting, observability, and operational reporting.

• Experience supporting GPU-enabled infrastructure for AI workloads.

• Knowledge of model serving, inference optimization, performance tuning, and scaling strategies.

• Understanding of Infrastructure-as-Code and cloud automation practices.

AI / Machine Learning Expertise

• Strong understanding of the machine learning lifecycle, including training, deployment, monitoring, and optimization.

• Experience supporting production AI systems in enterprise environments.

• Ability to partner effectively with Machine Learning Engineers and Data Scientists.

• Experience with any machine learning model type, including traditional ML, deep learning, generative AI, or LLM-based solutions.

Preferred Qualifications

• Experience supporting large-scale enterprise AI platforms.

• Experience operating AI workloads across multiple business units or enterprise environments.

• Familiarity with model governance, drift monitoring, and AI operational best practices.