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ML Ops Senior Engineer

2 months ago

Hyderabad, India MatrixOne Tech Solutions Full time

MLOps - DevOps Engineer


Experience : Total experience - 6 to 9 years.

Location ; Hyderabad

Model : Work from Office

Shift : India Business Hours - 10am to 6 pm

Position : Permanent Employee

Mandatory Experience : AWS products, Python, ML Libraries

Start : Immediate to 15 days notice period. Please do not apply if your notice period is more than this duration.


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.


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).