ML Ops AI Engineer II

AptivaCorp; Coppell (TX); Presencial; USD 120.000 - 160.000; Jornada completa

Descripción de la vacante

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

Formación

• 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.

Responsabilidades

• 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.

Conocimientos

Python

Automation

Kubernetes

Docker

Azure

ML concepts

Educación

Bachelor's or Master's in CS/Engineering

Herramientas

Kubeflow

Airflow

Azure ML Pipelines

MLflow

ONNX

TensorRT

Vertex AI

Prometheus

Grafana

ELK

Descripción del empleo

We have an Urgent opening for ML Ops AI Engineer II with one of our clients.

Duration : Long Term Contract-W2

MUST Need Only TX Locals

Expectation / How Much Computer Vision is Required?

Must have participated in Computer Vision projects.

Does not need to be training Computer Vision models from scratch, but should have been involved in Computer Vision initiatives.

GPU provisioning is a must-have.

Experience managing Kubernetes and Dockerfiles is required.

Experience handling large images and image/video streams.

Needs to be able to work independently.

Top Skills:

Python Automation & 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:

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.