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# Machine Learning Engineer

- **Company:** Robert Half
- **Location:** Glendale, AZ, United States
- **Experience:** Experienced
- **Contract:** Temporary contract
- **Skills:** Artificial Intelligence, Amazon Web Services, Automation of Tests, Microsoft Azure, Information Engineering, Monitoring of Systems, Python (Programming Language), Machine Learning, Tensorflow, Azure Machine Learning, Software Engineering, Web Services, Enterprise Software Applications, Pytorch, Scikit Learn, Machine Learning Operations, Restful APIs, Docker, Databricks, Microservices
- **Published:** October 8, 2026
- **Apply:** https://dejobs.org/x/x/4A0BAABB706C408BAE5486EA6DF6FD15/job/

## About the Role

* 4+ years of software engineering, machine learning engineering, or related experience.
 * Strong Python development skills.
 * Experience with TensorFlow, PyTorch, Scikit-learn, or similar frameworks.
 * Experience deploying models into production environments.
 * Knowledge of Docker and Kubernetes.
 * Experience with AWS, Azure, or GCP machine learning services.
 * Understanding of ML lifecycle management and model monitoring.
 * Experience building REST APIs or microservices.

Experience with Databricks, MLflow, SageMaker, Azure Machine Learning, or Vertex AI is highly desirable.

## Description

Robert Half is working with a client who is looking to hire a Machine Learning Engineer to help operationalize machine learning models as part of a growing enterprise AI program.

This role will sit between Data Science and Engineering and will focus on taking models from experimentation into reliable, scalable production environments.

Responsibilities

 * Develop and deploy machine learning models into production.
 * Build reusable ML pipelines for training, testing, deployment, and monitoring.
 * Partner with Data Scientists to productionize predictive models.
 * Develop APIs and services that expose machine learning capabilities to enterprise applications.
 * Monitor model performance, drift, and reliability.
 * Build automated testing and deployment processes for ML workloads.
 * Optimize model performance and infrastructure utilization.
 * Work with Data Engineering teams to establish reliable training and inference datasets.