Lead Automation Test Engineer - Machine Learning

Cigres Technologies; Bengaluru; Híbrido; INR 1.500.000 - 2.600.000; Jornada completa

Descripción de la vacante

Cigres Technologies in Bengaluru is seeking an experienced software test engineer to enhance QA across data-intensive applications. The role emphasizes robust test automation, ML/AI integration, and quality across pipelines and services.

You will work with Python, PyTest, and CI/CD, test APIs, databases, and models, and contribute to ML-Ops practices like experiment tracking and deployment monitoring in a cloud environment.

Formación

• 6–8 years in software testing and test automation.
• Hands-on Python with PyTest or similar frameworks.
• Knowledge of ML concepts, model lifecycle, and evaluation metrics.
• Experience testing data pipelines, APIs, microservices, databases.

Responsabilidades

• Design and implement test plans for data-intensive apps.
• Automate test suites and maintain CI/CD integrations.
• Validate ML models and data processing pipelines.
• Collaborate with data engineers and software developers.

Conocimientos

Software testing

Test automation

Python programming

ML/AI concepts

Model evaluation metrics

Data pipelines testing

APIs & microservices testing

CI/CD workflows

ML-Ops concepts

Cloud platforms knowledge

SQL / NoSQL databases

Git workflows

Herramientas

PyTest

Git

CI/CD tooling

ML-Ops tooling

Cloud platforms (Azure/AWS/Databricks)

Descripción del empleo

Role & responsibilities:

• 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.