ML Testing

Greetings

We are looking for a ML Tester with handson experience in validating AIML models and data pipelines in a Snowflake AWS SageMaker MLOps environment The role focuses on ensuring model correctness data quality reproducibility and CICDbased validation

Key Responsibilities

Validate ML model outputs against expected business and statistical outcomes

Perform functional regression and performance testing of ML pipelines and models

Design and execute test strategies for ML systems including data model and pipeline validation

Validate SageMaker pipelines training inference orchestration

Perform data quality checks using tools like Great Expectations Soda

Conduct result comparison diffs between model runs datasets or environments

Ensure test coverage across CICD pipelines including automated test stages

Validate integration between Snowflake SageMaker and downstream systems

Generate test reports validation summaries and audit artifacts

Required Skills

Strong experience in

ML testing Data testing QA for AI systems

Python testing frameworks pytest hypothesis

AWS SageMaker pipelines traininginference validation

MLOps concepts model lifecycle CICD versioning

Data quality validation tools Great Expectations Soda

Handson with

Result validation techniques diffs statistical checks parity validation

Report generation and test documentation

Basic Snowflake SQL for data validation

Good to Have

Experience in

Model reproducibility and traceability testing

MLflow Model Registry validation

Statistical parity bias validation checks

Streamlit or UI validation in ML workflows

Experience

3 to 5 years of relevant experience in ML Testing Data QA MLOps testing

Additional Expectations

Strong analytical and debugging skills

Understanding of endtoend ML lifecycle feature train infer deploy

Experience in working with Agile teams and CICD environments

Ability to work closely with Data ML and MLOps teams