NStarX - MLOps Engineer - AWS/Python

3 weeks ago

Hyderabad, India Nstarx India Private Limited Full time

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.

(ref:hirist.tech)

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