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Job Title: ML Engineer
Location: Malvern, PA
Long Term Contact
Can do Only W2, No C2C
Job Summary:
We are seeking an experienced Machine Learning Engineer with strong MLOps expertise on AWS to design, build, deploy, and maintain scalable machine learning solutions. The ideal candidate will have hands-on experience with AWS ML services, productionizing machine learning models, automated CI/CD pipelines, and end-to-end model lifecycle management.
The candidate should have strong knowledge of Machine Learning, DevOps, AWS cloud services, feature engineering, and production ML systems with a focus on reliability, performance, and cost optimization.
Key Responsibilities:
Machine Learning, MLOps, AWS, SageMaker, S3, Lambda, Step Functions, API Gateway, CodePipeline, CodeBuild, CI/CD, DevOps, Feature Engineering, ML Model Deployment, Model Lifecycle Management
Best Regards:
Bindu M
Phone:
Email:
Job Title: ML Engineer
Location: Malvern, PA
Long Term Contact
Can do Only W2, No C2C
Job Summary:
We are seeking an experienced Machine Learning Engineer with strong MLOps expertise on AWS to design, build, deploy, and maintain scalable machine learning solutions. The ideal candidate will have hands-on experience with AWS ML services, productionizing machine learning models, automated CI/CD pipelines, and end-to-end model lifecycle management.
The candidate should have strong knowledge of Machine Learning, DevOps, AWS cloud services, feature engineering, and production ML systems with a focus on reliability, performance, and cost optimization.
Key Responsibilities:
- Design, develop, deploy, and maintain scalable machine learning solutions.
- Build and manage end-to-end ML pipelines using AWS cloud services.
- Implement and manage ML model lifecycle processes from development through production.
- Develop, deploy, and monitor machine learning models in production environments.
- Build scalable ML workflows using AWS SageMaker, S3, Lambda, Step Functions, and API Gateway.
- Perform feature engineering and optimize machine learning models for production use.
- Implement CI/CD pipelines using AWS CodePipeline and CodeBuild.
- Improve system reliability, performance, scalability, and cost efficiency.
- Monitor ML applications and troubleshoot production issues.
- Collaborate with data scientists, engineers, and business teams to deliver ML solutions.
- Machine Learning
- MLOps
- AWS Cloud Services
- AWS SageMaker
- Amazon S3
- AWS Lambda
- AWS Step Functions
- AWS API Gateway
- CI/CD Implementation
- AWS CodePipeline
- AWS CodeBuild
- Feature Engineering
- Machine Learning Model Deployment
- Model Monitoring
- End-to-End ML Lifecycle Management
- Productionizing ML Models
- DevOps Practices
- Cloud-based ML Architecture
- 8-10 years of experience in Machine Learning Engineering, MLOps, or related fields.
- Experience building enterprise-scale machine learning platforms.
- Strong experience with AWS-based ML solutions.
- Experience implementing automation and deployment frameworks.
- Experience optimizing ML workloads for performance and cost.
- Experience working with production-grade ML systems.
- Strong analytical and problem-solving skills.
- Excellent communication and collaboration abilities.
- Ability to work with cross-functional technical teams.
- Strong ownership and attention to detail.
- Ability to troubleshoot complex technical issues.
- Adaptability to evolving technologies and business requirements.
Machine Learning, MLOps, AWS, SageMaker, S3, Lambda, Step Functions, API Gateway, CodePipeline, CodeBuild, CI/CD, DevOps, Feature Engineering, ML Model Deployment, Model Lifecycle Management
Best Regards:
Bindu M
Phone:
Email: