ML Ops AI Engineer II - GPU, Kubernetes, Vision (TX Local)

AptivaCorp; Coppell (TX); On-site; USD 120,000 - 160,000; Full time

Job summary

AptivaCorp is seeking an ML Ops AI Engineer II in Texas to own the path from model experimentation to reliable production deployment. You will build and maintain ML infrastructure, pipelines, and automation, packaging workloads with Docker and Kubernetes, and implementing CI/CD for ML workflows.

Ideal candidates have 3–5 years in MLOps, hands-on with MLflow, Kubeflow or Airflow, and strong Python skills. This long-term contract position requires TX locals and GPU compute management for training

Qualifications

• 3-5 years of experience in MLOps, ML platform, or ML/AI infrastructure engineering.
• Proven track record deploying and operating models in production.
• Experience with cloud environments (Azure) and GPU compute.
• Hands-on experience with experiment tracking and model versioning (MLflow).
• Strong Python skills and familiarity with infrastructure-as-code and automation.

Responsibilities

• Own the path from model experimentation to production deployment.
• Build and maintain the infrastructure, pipelines, and automation for efficient deployments.
• Package and orchestrate workloads using Docker and Kubernetes.
• Design and operate CI/CD workflows for training, packaging, and deploying models.
• Implement experiment tracking and model/versioning for reproducibility.
• Develop dashboards, drift detection, and production monitoring.
• Provision and optimize GPU compute and cloud resources for training and inference.
• Collaborate with model developers to streamline production.

Skills

Python

Automation

Kubernetes

Docker

Azure

ML concepts

Education

Bachelor's or Master's in CS/Engineering

Tools

Kubeflow

Airflow

Azure ML Pipelines

MLflow

ONNX

TensorRT

Vertex AI

Prometheus

Grafana

ELK

Job description

AptivaCorp is seeking an ML Ops AI Engineer II in Texas to own the path from model experimentation to reliable production deployment. You will build and maintain ML infrastructure, pipelines, and automation, packaging workloads with Docker and Kubernetes, and implementing CI/CD for ML workflows.

Ideal candidates have 3–5 years in MLOps, hands-on with MLflow, Kubeflow or Airflow, and strong Python skills. This long-term contract position requires TX locals and GPU compute management for training