QA Lead

Role Specification 

Role Description

We are looking for a Test Lead to define and drive the end-to-end QA strategy for our AI-powered platform. This role involves testing components across the stack—including ML models/backend, backend services, and frontend interfaces—to ensure accuracy, performance, security, and reliability. You will lead a team of testers, collaborate cross-functionally, and build automation pipelines aligned with CI/CD practices.

Role Title

SW QA Engineer/Tester

Role Purpose

What the role entails

(Key Responsibilities) 

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  Define and own the comprehensive test plan for ML inference, backend workflows, and frontend user experience.

  
  
  


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  Identify test scenarios for real-time sensor data pipelines, anomaly/event detection, historical playback, and UI s/configs.

  
  
  


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  Design test cases with focus on modularity, reusability, and traceability to requirements.

  
  
  


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  Define and track test coverage, maintain regression test suites, and handle test data design.

  
  
  
  
  
  

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  Validate ML outputs (event classification, anomaly scores) against labeled test datasets.

  
  


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  Perform accuracy testing, A/B comparison, drift detection, and model regression checks.

  
  


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  Test model integration in real-time pipelines (latency, fault tolerance, failover behavior).

  
  
  
  

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  Validate backend APIs that consume ML outputs, transform data, and expose events to the UI.

  
  


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  Test REST/gRPC endpoints, data transformations, streaming services (Kafka), and storage layers (PostgreSQL, Redis).

  
  


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  Design load, concurrency, and recovery tests for backend services.

  
  
  
  

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  Verify UI correctness for event visualization, timeline navigation, and control tools.

  
  


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  Automate UI workflows using tools like Cypress, Playwright, or Selenium.

  
  


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  Ensure responsiveness, accessibility, and data consistency in real-time dashboards.

  
  
  
  

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  Lead setup of test automation pipelines integrated with CI/CD (GitHub Actions, GitLab CI, etc.).

  
  


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  Own the implementation of smoke, sanity, regression, and performance suites.

  
  


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  Collaborate with DevOps for observability in test environments, test parallelization, and reporting.

  
  
  
  

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  Mentor QA engineers and drive best testing practices across the platform.

  
  


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  Collaborate with ML, backend, frontend, and product teams to review requirements and raise quality gates.

  
  


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  Drive root cause analysis and support release readiness with QA sign-off.

  
  

What we are looking for in you

(Skills, Knowledge, Special Attributes, Mobility)

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  4 years in software testing with at least 1 years in a lead role.

  
  
  


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  Experience testing ML model pipelines, data-driven APIs, and real-time UI platforms.

  
  


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  Strong command over test design techniques, risk-based testing, and shift-left testing practices.

  
  


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  Proficient in Python or JavaScript for writing automation scripts.

  
  


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  Hands-on with tools such as Postman, JMeter, Pytest, Cypress, or Playwright.

  
  


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  Familiarity with Kafka, REST/gRPC, Docker, and Linux environments.

  
  


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  Knowledge of CI/CD, test versioning, and defect lifecycle management tools (e.g., JIRA, TestRail).

  
  


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  Experience with model explainability testing (SHAP, LIME) or model monitoring tools.

  
  


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  Exposure to edge device testing, cloud deployments (GCP), and real-time event handling systems.

  
  


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  Knowledge of performance engineering and security testing basics.

  
  

Location

Aurangabad

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  Driven by innovation, design, plan and implement Data & (Gen)AI architectures including the associated roadmaps, where you work together with Enterprise Architects and Senior Management up to C-Level on the customer side

  
  
  


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  Design and set up experiments for collecting, labeling analyzing data of different kinds of events from fiber based sensor solutions 

  
  
  


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  Assess the effectiveness and accuracy of data sources and data gathering techniques

  
  


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  Develop automation methods for faster labeling of collected data

  
  


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  Create architectural approaches for AI software and hardware integration

  
  


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  Define AI solution objectives and ensure alignment with business outcomes

  
  


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  Implement best practices for AI testing, deployment, and maintenance

  
  


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  Develop Machine Learning and Deep Learning methods for event detection and classification 

  
  


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  Develop efficient frameworks for  training and evaluating the event detection/classification models

  
  


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  Track the innovations in AI/ML technologies and algorithms/processing/ computation and its implementation to develop next generation fiber sensing solution

  
  


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  Review and Optimize existing code/algorithm

  
  


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  Create systematic plans to run the experiments to fine-tune the event detection/classification models

  
  


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  Plan the tasks and provide estimates and  timelines

  
  


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  Support marketing/sales team with application notes, white papers and presentations

  
  


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  Contribute to simulation model development and data analysis of various sensor solutions

  
  


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  Design, Develop and optimize CNN-based models for event detection and classification using spectrograms and other signal-derived image formats.

  
  


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  Apply computer vision techniques to identify spatial and temporal patterns in DAS data visualizations.

  
  


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  Use deep learning architectures (e.g., CNN, RNN, LSTM) to model sequential and spatial aspects.