MLOPs QE

Essential Skills
Candidates should have 5+ years of experience including minimum 2 year of experience in data domain.
Candidate should be good in SQL and intermediate level of experience in Python
Candidate should have knowledge in Machine learning lifecycle including data collection, data preparation, model training, model execution and model monitoring.
Candidate should have experience in cloud platform: AWS/GCP/Azure with focus on MLOps.
Candidate should have experience in data validation process with focus on ML model training and model inferencing
Candidate should have experience in Validating the ML training and inferencing platform. Validate integrity of services/tools like S3 and sagemaker, Glue and S3, EMR and sagemaker etc. Validating accessibility of code from code versioning tool. Validate automated model training ,registering, approval and deployment pipeline.
Ability to analyze results from the test set and create defects based on result patterns.
Work with developers to design specific testing strategies for features being developed and automate them.
Hands on experience in constructing simple to complex SQLs with joins, analytics functions, sub-queries
Good exposure to agile testing process.

Preferred:

Good to have knowledge in model monitoring process and tools
Business context and experience within (---- ) model ecosystems will be a major plus.
Familiarity with Containerization (Docker)

Good to have experience in Machine learning/Deep learning python libraries like Scikit-learn, pandas, numpy etc.

Skills:

• Oracle
• Test Design
• Banking

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