Required Skills:

- Python Test Automation
- ML Testing & Validation
- CI/CD & MLOps
- Data Pipeline Testing
- API & Microservices Testing
- Cloud Platforms
- SQL & Databases

Nice to Have:

- MLflow / ML Platforms
- Data Quality Tools
- Performance Testing
- Docker & Kubernetes

Designation | Lead, Automation Test Engineer Qualification | B.E or B.Tech Experience (in years) | 6 Job Description | We are seeking a Lead Software Engineer – Machine Learning Testing with 6–8 years of experience in software quality engineering, including hands-on validation of data-driven and machine learning applications. The ideal candidate combines strong Python-based test automation skills with practical knowledge of data quality, model evaluation, ML pipelines, and production monitoring. Key Responsibilities Machine Learning Validation & Verification • Design and execute end-to-end V&V; strategies for classical ML use cases, including regression, classification, clustering, forecasting, and anomaly detection. • Define risk-based test plans, test scenarios, test cases, acceptance criteria, and traceability across the ML lifecycle. • Validate training, validation, and test datasets for completeness, accuracy, consistency, representativeness, leakage, imbalance, and schema quality. • Evaluate model performance using business-appropriate and technical metrics such as precision, recall, F1-score, ROC-AUC, MAE, RMSE, MAPE/SMAPE, and calibration. • Perform robustness, boundary, negative, bias, explainability, reproducibility, and non-regression testing. • Validate model behavior across segments, edge cases, unseen data, and changing operating conditions. • Ensure V&V; coverage from data preparation and experimentation through deployment, monitoring, retraining, and retirement. Test Automation & ML Pipeline Quality • Build scalable Python and PyTest-based automation frameworks for data, model, API, batch, and end-to-end testing. • Automate checks for data pipelines, feature engineering, model artifacts, inference services, and integration points.



• Integrate automated quality gates into CI/CD and ML-Ops pipelines. • Develop test suites for APIs, microservices, scheduled pipelines, and cloud-based ML workloads. • Perform sanity, functional, integration, regression, performance, resilience, and user acceptance testing across QA, UAT, and production-like environments. • Create reusable test utilities, synthetic test datasets, mocks, fixtures, and baseline comparisons. ML Monitoring & Production Quality • Define and validate monitoring for model quality, data drift, concept drift, prediction distribution, latency, throughput, failures, and service availability. • Verify alert thresholds, dashboards, logging, lineage, model versioning, and rollback readiness. • Analyze production issues and quality signals to support root-cause analysis, corrective actions, and continuous improvement. • Collaborate with Data Scientists, ML Engineers, Software Engineers, Product Managers, and Governance teams to define release readiness and quality evidence. Quality Assurance & Reporting • Analyze test results, identify quality risks, and communicate actionable insights to engineering and business stakeholders. • Log, prioritize, and track defects using standard defect-management tools. • Maintain high-quality documentation for test strategies, test evidence, validation reports, model cards, and release recommendations. • Contribute to test standards, review practices, and continuous improvement of AI/ML quality engineering. Required Skills & Experience • 6–8 years of experience in software testing and test automation, with practical exposure to AI/ML or data-intensive applications.



• Strong programming expertise in Python and hands-on experience with PyTest or equivalent test frameworks. • Good understanding of supervised and unsupervised machine learning concepts, model lifecycle, feature engineering, and statistical validation. • Hands-on knowledge of model evaluation metrics for classification, regression, forecasting, and imbalanced datasets. • Experience testing data pipelines, APIs, microservices, databases, and batch-processing workflows. • Experience with Git-based workflows and CI/CD pipelines. • Working knowledge of ML-Ops concepts such as model registry, experiment tracking, deployment, monitoring, drift, and retraining. • Knowledge of Azure, AWS, Databricks, or equivalent cloud/data platforms. • Familiarity with SQL and relational or NoSQL databases. • Strong verbal and written communication skills, with experience working across multi-stakeholder and onshore–offshore delivery models. • Qualification: B.E./B.Tech or an equivalent degree in Computer Science, Engineering, Data Science, or a related discipline. Preferred / Nice-to-Have • Experience with MLflow, Azure Machine Learning, Amazon SageMaker, Databricks, or similar ML platforms. • Exposure to data-quality and validation tools such as Great Expectations, Pandera, or equivalent frameworks. • Knowledge of responsible AI testing, including fairness, explainability, transparency, privacy, and governance controls. • Experience with performance testing tools such as JMeter, k6, or Locust for inference APIs and ML services. • Familiarity with Docker, Kubernetes, observability platforms, and production support for ML workloads. • Exposure to GenAI or agentic AI testing is an added advantage but is not the primary focus of this role. Soft Skills • Analytical and critical thinking • Curiosity and continuous learning • Robust debugging and problem-solving mindset • Strategic communication, active listening, and translation of technical quality risks into business impact • Collaboration, ownership, and attention to detail

📌 Lead Automation Test Engineer - Machine Learning - M L (India)
🏢 Cigres Technologies
📍 India