Vacancy description
STL Digital Consulting
India, India
QA Lead Engineer in India, India is listed on Jobeax. Browse 30,000+ vacancies available.
Role description
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)
Test Strategy & Planning
Define and own the comprehensive test plan for ML inference, backend workflows, and frontend user experience.
Identify test scenarios for real-time sensor data pipelines, anomaly/event detection, historical playback, and UI s/configs.
Design test cases with focus on modularity, reusability, and traceability to requirements.
Define and track test coverage, maintain regression test suites, and handle test data design.
ML Model Testing
Validate ML outputs (event classification, anomaly scores) against labeled test datasets.
Perform accuracy testing, A/B comparison, drift detection, and model regression checks.
Test model integration in real-time pipelines (latency, fault tolerance, failover behavior).
Backend & API Testing
Validate backend APIs that consume ML outputs, transform data, and expose events to the UI.
Test REST/gRPC endpoints, data transformations, streaming services (Kafka), and storage layers (PostgreSQL, Redis).
Design load, concurrency, and recovery tests for backend services.
Frontend Testing
Verify UI correctness for event visualization, timeline navigation, and control tools.
Automate UI workflows using tools like Cypress, Playwright, or Selenium.
Ensure responsiveness, accessibility, and data consistency in real-time dashboards.
Automation & DevOps Alignment
Lead setup of test automation pipelines integrated with CI/CD (GitHub Actions, GitLab CI, etc.).
Own the implementation of smoke, sanity, regression, and performance suites.
Collaborate with DevOps for observability in test environments, test parallelization, and reporting.
Leadership & Collaboration
Mentor QA engineers and drive best testing practices across the platform.
Collaborate with ML, backend, frontend, and product teams to review requirements and raise quality gates.
Drive root cause analysis and support release readiness with QA sign-off.
What we are looking for in you
(Skills, Knowledge, Special Attributes, Mobility)
Aurangabad
Other details
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
Design and set up experiments for collecting, labeling analyzing data of different kinds of events from fiber based sensor solutions
Assess the effectiveness and accuracy of data sources and data gathering techniques
Develop automation methods for faster labeling of collected data
Create architectural approaches for AI software and hardware integration
Define AI solution objectives and ensure alignment with business outcomes
Implement best practices for AI testing, deployment, and maintenance
Develop Machine Learning and Deep Learning methods for event detection and classification
Develop efficient frameworks for training and evaluating the event detection/classification models
Track the innovations in AI/ML technologies and algorithms/processing/ computation and its implementation to develop next generation fiber sensing solution
Review and Optimize existing code/algorithm
Create systematic plans to run the experiments to fine-tune the event detection/classification models
Plan the tasks and provide estimates and timelines
Support marketing/sales team with application notes, white papers and presentations
Contribute to simulation model development and data analysis of various sensor solutions
Design, Develop and optimize CNN-based models for event detection and classification using spectrograms and other signal-derived image formats.
Apply computer vision techniques to identify spatial and temporal patterns in DAS data visualizations.
Use deep learning architectures (e.g., CNN, RNN, LSTM) to model sequential and spatial aspects.
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