![Nstarx India Private Limited](https://media.trabajo.org/img/noimg.jpg)
NStarX - MLOps Engineer - AWS/Python
3 weeks ago
Job Description :
Key Qualifications :
- Bachelor's or Master's degree in Computer Science, Engineering, Data Science, or a related field.
- Minimum of 3 years of experience in machine learning engineering, with a proven track record of deploying scalable ML solutions on AWS.
- Strong proficiency in Python, with hands-on experience in ML libraries (e.g., TensorFlow, PyTorch, Scikit learn).
Technical Skills and Experience :
AWS Expertise :
- Extensive experience with AWS services related to ML and data processing, such as AWS Sagemaker, Lambda, S3, Glue, and EC2.
- Proficiency in building and managing data processing pipelines utilizing AWS technologies.
CI/CD Pipeline Development :
- Solid understanding of CI/CD principles and experience in building and maintaining CI/CD pipelines specifically for ML workflows on AWS.
- Familiarity with tools like AWS CodePipeline, CodeBuild, and Jenkins or GitLab for automation.
ML Engineering Architecture Optimization :
- Experience in optimizing ML architectures on AWS, including effective use of Lambda functions for scalable, event-driven ML processes.
- Knowledge of containerization technologies (Docker, Kubernetes) and serverless architectures.
Model Governance and Deployment :
- Proven ability to set up model governance frameworks ensuring model quality, reproducibility, and auditability.
- Experience in developing model deployment pipelines, ensuring models are robustly tested, version-controlled, and easily rolled out or rolled back.
ML Model Development and Testing :
- Strong background in developing, evaluating, and iterating on ML models to meet project objectives.
- Proficiency in model testing techniques, including A/B testing and performance monitoring.
Responsibilities :
- Design, develop, and maintain scalable data processing and ML pipelines on AWS, ensuring best practices in data handling and processing.
- Implement and optimize CI/CD pipelines for automated testing, integration, and deployment of ML models.
- Collaborate with data scientists and engineers to optimize ML engineering architecture on AWS, focusing on efficiency and scalability.
- Establish robust model governance practices, including model versioning, auditing, and compliance with industry standards.
- Continuously research and apply the latest ML technologies and methodologies to enhance project outcomes.
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