ML Ops Test Engineer
3 weeks ago
Job Title: ML Ops Test Engineer
Job Summary:
We are seeking a highly skilled ML Ops Test Engineer to join our team at Siri InfoSolutions Inc. As an ML Ops Test Engineer, you will be responsible for ensuring the reliability, scalability, and performance of machine learning models in production.
Key Responsibilities:
- Develop and Execute Tests: Design and implement test cases to evaluate the performance, robustness, and reliability of ML models.
- Statistical Analysis: Perform statistical analysis and fine-tuning using test results to improve model accuracy and efficiency.
- Model Monitoring: Continuously monitor the health and performance of deployed models, identifying and addressing issues proactively.
- Collaboration: Work closely with data scientists, software engineers, and DevOps teams to integrate testing processes into the ML lifecycle.
- Automation: Develop and maintain automated testing frameworks and CI/CD pipelines for ML models.
- Documentation: Document test plans, procedures, and results to ensure transparency and reproducibility.
- Stay Updated: Keep abreast of the latest developments in ML, MLOps, and testing methodologies.
Requirements:
- Programming Languages: Proficiency in Python, Java, or Scala.
- Machine Learning Frameworks: Experience with TensorFlow, PyTorch, scikit-learn, or Keras.
- Data Engineering: Knowledge of data pipelines, data processing, and storage solutions like Hadoop, Spark, and Kafka.
- Cloud Computing: Familiarity with cloud platforms such as Azure.
- Containerization and Orchestration: Expertise in Docker and Kubernetes.
- Version Control: Proficiency with Git and CI/CD tools.
- Analytical Skills: Strong analytical and problem-solving skills to interpret test results and improve model performance.
Preferred Qualifications:
- Experience: Previous experience in MLOps, software testing, or a related field.
- Certifications: Relevant certifications such as Azure AI Engineer Associate.
Soft Skills:
- Communication: Excellent verbal and written communication skills to collaborate effectively with cross-functional teams.
- Attention to Detail: Meticulous attention to detail to ensure the accuracy and reliability of test results.
- Adaptability: Ability to adapt to rapidly changing technologies and methodologies in the ML and MLOps landscape.
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