{"id":1428138,"url":"https://alion.io/job/infobeans-technologies-infobeans-aiml-quality-assurance-specialist-python","title":"InfoBeans - AI/ML Quality Assurance Specialist - Python","company":{"id":59407,"name":"InfoBeans Technologies","domain":"infobeans.ai","url":"https://alion.io/company/infobeans-technologies","size_band":null,"is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"SmartRecruiters","truth_index":null},"role":"QA","role_family":"QA","seniority":"senior","employment_type":"full_time","work_mode":"on_site","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Indore, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":{"min_usd":11500,"max_usd":29000,"period":"year","method":"role_seniority_country_remote_cell","sample_n":28},"experience_years_min":5,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Azure","optional":false},{"name":"CI/CD","optional":false},{"name":"Databricks","optional":false},{"name":"Delta Lake","optional":false},{"name":"MLFlow","optional":false},{"name":"pySpark","optional":false},{"name":"Python","optional":false},{"name":"Spark","optional":false},{"name":"Feature Store","optional":true}],"status":"live","first_seen_at":"2026-09-28T22:00:30Z","employer_posted_date":null,"last_verified_at":"2026-09-28T22:00:30Z","board_verified":false,"closed_at":null,"days_open":17,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":17},"description":"Job Description\n\nWe are seeking skilled AI/ML QA Specialists with strong Databricks experience to ensure the quality, reliability, and regulatory readiness of AI/ML platforms. This role will focus on end-to-end testing of CCAR and ESG projects, covering data pipelines, feature engineering, model training, validation, deployment, and monitoring.\n\nKey Responsibilities\n\nModel Development Platform QA : \n\n Validate data ingestion, feature engineering, and training pipelines built on Databricks (Spark, Delta, MLflow).\n Design and execute QA strategies for dataset quality, schema validation, lineage, feature consistency, drift checks, and reproducibility.\n Test MLflow experiments, model versioning, and artifacts for completeness and traceability.\n Ensure compliance with model risk management (MRM), audit, and documentation standards.\n\nModel Execution / Production Platform QA\n\n Test model deployment pipelines, including batch and real-time model execution.\n Validate model scoring accuracy, performance, data contracts, SLAs, error handling, and fallback logic.\n Perform regression, performance, and volume testing for production workloads.\n\nAutomation & Tooling\n\n Build and maintain automated test frameworks for data and ML pipelines (Databricks notebooks, PySpark, Python).\n Implement data-driven QA checks (DQ rules, nulls, thresholds, statistical validation).\n Integrate QA into CI/CD pipelines for ML workflows.\n\nRequired Skills\n\n 5 - 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