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ML Ops Senior Engineer

2 months ago

Hyderabad, India MatrixOne Tech Solutions Full time
MLOps - DevOps Enginee r
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 in the below mentioned technologies - All minimum of 2 -3 years of valid development experience.
ML Flow
Apache
Airflow,
Kubeflow,
DevOps any (Jenkins CI/CD or DevOps CI/CD),
Prometheus &
Grafana
Kubernates
Docker
AWS products
Python
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).