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# Machine Learning Engineer

- **Company:** Rapid Eagle Inc.
- **Location:** United States (Remote available)
- **Experience:** Expert
- **Salary:** $145,600.0 - $166,400.0
- **Contract:** Permanent contract
- **Skills:** Amazon Web Services, Microsoft Azure, Cloud Computing, Computer Programming, Continuous Integration, Information Engineering, Data Structures, Distributed Computing Environment, Python (Programming Language), Machine Learning, Recommender Systems, Tensorflow, SQL Databases, Web Services, Data Processing, Feature Engineering, Data Ingestion, Pytorch, Apache Spark, Model Validation, Scikit Learn, Kubernetes, Xgboost, Machine Learning Operations, Restful APIs, Docker, Microservices
- **Published:** June 10, 2026
- **Apply:** https://www.indeed.com/viewjob?jk=04eb778a23284ca5

## About the Role

Do you have experience in Web services design?, * 5+ years of experience in Machine Learning Engineering or Data Science.

 * Strong programming skills in Python.
 * Experience with ML frameworks such as TensorFlow, PyTorch, Scikit-Learn, XGBoost.
 * Strong understanding of machine learning algorithms, statistics, and data structures.
 * Experience with SQL, data processing, and feature engineering.
 * Hands-on experience with cloud platforms such as AWS, Azure, or GCP.
 * Experience with Docker, Kubernetes, CI/CD pipelines, and MLOps tools.
 * Knowledge of REST APIs, microservices architecture, and model deployment.
 * Experience working with distributed computing frameworks such as Spark.

## Description

We are seeking a highly skilled Machine Learning Engineer to design, develop, deploy, and maintain scalable machine learning solutions that drive business value. The ideal candidate will have strong expertise in machine learning algorithms, data engineering, model deployment, and cloud technologies., * Design, develop, and deploy machine learning models for predictive analytics, classification, recommendation systems, and NLP applications.

 * Build and optimize end-to-end ML pipelines for data ingestion, feature engineering, model training, validation, and deployment.
 * Collaborate with Data Scientists, Data Engineers, and business stakeholders to translate business requirements into ML solutions.
 * Develop and maintain scalable APIs and microservices for model serving.
 * Monitor model performance, retrain models, and implement MLOps best practices.
 * Work with large-scale structured and unstructured datasets.
 * Optimize model accuracy, scalability, and reliability in production environments.
 * Implement CI/CD pipelines for ML model deployment and lifecycle management.


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