Job Title: ML Ops AI Engineer II
Location: Coppell, TX
Work Model: Hybrid - Monday & Friday Remote; Tuesday-Thursday Onsite
Duration: 3-6 months Contract, with potential extension or conversion to Full-Time
For the MLOps Engineer role, based on the managers feedback, I'd narrow the must-haves down to:
Top 3 Must-Haves
Computer Vision / AI Model Background
Experience working with Computer Vision or AI models.
Doesn't need to be a pure CV Engineer, but must understand how models are trained, evaluated, and deployed.
Production Deployment Experience
Taking models from experimentation to production.
Real-world deployment, monitoring, troubleshooting, scaling, and support.
Not just training models in notebooks.
Cloud + MLOps Infrastructure
Azure preferred.
Kubernetes, Docker, CI/CD.
MLflow, deployment pipelines, monitoring, model versioning.
Experience supporting AI workloads in production environments.
What I'd Put in Recruiter Notes
MLOps Engineer Must-Haves
Hands-on experience deploying AI/Computer Vision models into production.
Strong MLOps background (Kubernetes, Docker, CI/CD, MLflow, monitoring, model versioning).
Azure cloud experience and familiarity with ML infrastructure.
Ability to support both the model side and the deployment/platform side.
Project-based resume with clear examples of production AI deployments.
What The Manager Seems to Care About
"Don't send me a DevOps engineer who happens to know Kubernetes."
He wants someone who understands:
AI models
Computer Vision concepts
Production deployment
Cloud infrastructure
Job Description:
- Experiment-to-Production: Own the path from model experimentation to reliable production deployment.
- ML Infrastructure: Build and maintain the infrastructure, pipelines, and automation that let models deploy efficiently and reliably.
- Containerization & Orchestration: Package and orchestrate workloads using Docker and Kubernetes.
- CI/CD for ML: Design and operate CI/CD workflows for training, packaging, and deploying models.
- Experiment Tracking & Versioning: Implement experiment tracking and model/feature versioning so results are reproducible.
- Monitoring & Observability: Build dashboards, drift detection, and production monitoring to keep systems stable and performant.
- GPU & Compute Management: Provision and optimize GPU compute and cloud resources for training and inference.
- Cost & Scale: Make deployments reproducible, scalable, observable, and cost-effective.
- Collaboration: Work closely with model developers, applying AI/ML understanding to streamline their path to production.
Qualifications
Qualifications we're looking for.
Experience
- 3-5 years of experience in MLOps, ML platform, or ML/AI infrastructure engineering.
- Proven track record deploying and operating models in production.
Skills
- Experience with cloud environments (Azure) and GPU compute.
- Strong containerization and orchestration skills (Docker, Kubernetes).
- Experience building CI/CD workflows for ML or software systems.
- Hands-on experience with experiment tracking and model versioning (e.g. MLflow).
- Experience building dashboards, drift detection, and production monitoring.
- Strong Python skills and familiarity with infrastructure-as-code and automation.
Abilities
- Sufficient understanding of AI/ML concepts to work closely with model developers.
- Able to make deployments reproducible, scalable, observable, and cost-effective.
- Strong problem-solving skills and ability to work in a fast-paced, agile environment.
Education
- Bachelor's or Master's degree in Computer Science, Engineering, or a related field.
Preferred Qualifications
- Experience with ML pipeline / orchestration tools (Kubeflow, Airflow, Azure ML Pipelines, or Vertex AI).
- Experience optimizing inference (quantization, ONNX, TensorRT) and edge or real-time deployment.
- Familiarity with observability tooling (Prometheus, Grafana, ELK) and model performance monitoring.
- Experience with feature stores, data versioning, and reproducible data pipelines.
- Exposure to retail, IoT, or computer vision production systems.