MLOPS AI ENGINEER II

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

1. 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.

2. Production Deployment Experience

   • Taking models from experimentation to production.

   • Real-world deployment, monitoring, troubleshooting, scaling, and support.

   • Not just training models in notebooks.

3. 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.