QA Engineer – Machine Learning Systems

Position : QA Engineer – Machine Learning Systems (5 - 7 years)

Location : Remote (Company in Mumbai)

Company : Big Rattle Technologies Private Limited

Immediate Joiners only.

Summary :

The QA Engineer will own quality assurance across the ML lifecycle—from raw data validation through feature engineering checks, model training / evaluation verification, batch prediction / optimization validation, and end-to-end (E2E) workflow testing. The role is hands-on with Python automation, data profiling, and pipeline test harnesses in Azure ML and Azure DevOps. Success means probably correct data, models, and outputs at production scale and cadence.

Key Responsibilities :

Test Strategy & Governance

○ Define an ML-specific Test Strategy covering data quality KPIs, feature consistency

checks, model acceptance gates (metrics + guardrails), and E2E run acceptance

(timeliness, completeness, integrity).

○ Establish versioned test datasets & golden baselines for repeatable regression of

features, models, and optimizers.

Data Quality & Transformation

Validate raw data extracts and landed data lake data : schema / contract checks, null / outlier thresholds, time-window completeness, duplicate detection, site / material coverage.

Validate transformed / feature datasets : deterministic feature generation, leakage detection, drift vs. historical distributions, feature parity across runs (hash or statistical similarity tests).

Implement automated data quality checks (e.g., Great Expectations / pytest + Pandas / SQL) executed in CI and AML pipelines.

Model Training & Evaluation

Verify training inputs (splits, windowing, target leakage prevention) and hyperparameter configs per site / cluster.

Automate metric verification (e.g., MAPE / MAE / RMSE, uplift vs. last model, stability tests) with acceptance thresholds and champion / challenger logic.

Validate feature importance stability and sensitivity / elasticity sanity checks (price / volume monotonicity where applicable).

Gate model registration / promotion in AML based on signed test artifacts and reproducible metrics.

Predictions, Optimization & Guardrails

Validate batch predictions : result shapes, coverage, latency, and failure handling.

Test model optimization outputs and enforced guardrails : detect violations and prove idempotent writes to DB.

Verify API push to third party system (idempotency keys, retry / backoff, delivery receipts).

Pipelines & E2E

Build pipeline test harnesses for AML pipelines (data-gen nightly, training weekly,

prediction / optimization) including orchestrated synthetic runs and fault injection

(missing slice, late competitor data, SB backlog).

Run E2E tests from raw data store ->

ADLS ->

AML ->

RDBMS ->

APIM / Frontend; assert

freshness SLOs and audit event completeness (Event Hubs ->

ADLS immutable).

Automation & Tooling

Develop Python-based automated tests (pytest) for data checks, model metrics, and API contracts; integrate with Azure DevOps (pipelines, badges, gates).

Implement data-driven test runners (parameterized by site / material / model-version) and store signed test artifacts alongside models in AML Registry.

Create synthetic test data generators and golden fixtures to cover edge cases (price gaps, competitor shocks, cold starts).

Reporting & Quality Ops

Publish weekly test reports and go / no-go recommendations for promotions; maintain a defect taxonomy (data vs. model vs. serving vs. optimization).

Contribute to SLI / SLO dashboards (prediction timeliness, queue / DLQ, push success, data drift) used for release gates.

Required Skills (hands-on experience in the following) :

Python automation (pytest, pandas, NumPy), SQL (PostgreSQL / Snowflake), and CI / CD (Azure

DevOps) for fully automated ML QA.

Strong grasp of ML validation : leakage checks, proper splits, metric selection

(MAE / MAPE / RMSE), drift detection, sensitivity / elasticity sanity checks.

Experience testing AML pipelines (pipelines / jobs / components), and message-driven integrations

(Service Bus / Event Hubs).

API test skills (FastAPI / OpenAPI, contract tests, Postman / pytest-httpx) + idempotency and retry

patterns.

Familiar with feature stores / feature engineering concepts and reproducibility.

Solid understanding of observability (App Insights / Log Analytics) and auditability requirements.

Required Qualifications :

Bachelor’s or Master’s degree in Computer Science, Information Technology, or related field.

5–7+ years in QA with 3+ years focused on ML / Data systems (data pipelines + model validation).

Certification in Azure Data or ML Engineer Associate is a plus.

Why should you join Big Rattle?

Big Rattle Technologies specializes in AI / ML Products and Solutions as well as Mobile and Web Application Development. Our clients include Fortune 500 companies. Over the past 13 years, we have delivered multiple projects for international and Indian clients from various industries like FMCG, Banking and Finance, Automobiles, Ecommerce, etc. We also specialise in Product Development for our clients.

Big Rattle Technologies Private Limited is ISO 27001 : 2022 certified and CyberGRX certified.

What We Offer :

Opportunity to work on diverse projects for Fortune 500 clients.

Competitive salary and performance-based growth.

Dynamic, collaborative, and growth-oriented work environment.

Direct impact on product quality and client satisfaction.

5-day hybrid work week.

Certification reimbursement.

Healthcare coverage.

How to Apply :

Interested candidates are invited to submit their resume detailing their experience. Please detail out your work experience and the kind of projects you have worked on. Ensure you highlight your contributions and accomplishments to the projects.

Send your resume to jobs@bigrattle.com with 'Application for QA Engineer' in the subject line.