Job description

Job Summary

We are seeking a Vision AI QA Engineer to design, validate, and automate testing for AI-powered platforms, including computer vision solutions, machine learning models, and GenAI/RAG applications. The ideal candidate will possess a strong understanding of AI system behavior and apply robust quality engineering practices to ensure accuracy, reliability, performance, security, and production readiness across the end-to-end AI solution lifecycle. They will be responsible for developing test strategies, validating AI outcomes, automating test frameworks, and ensuring high-quality releases for Vision AI products.

Key Responsibilities

Own end-to-end quality assurance for Vision AI platforms and applications, covering UI, APIs, workflows, integrations, AI models, and production releases.Define and implement test strategies, test plans, regression suites, quality metrics, and release gates for Vision AI solutions.Execute functional, integration, regression, exploratory, system, and user acceptance testing across the application stack.Develop and maintain automated test frameworks for UI, API, and backend services using tools such as Python, Selenium, and AI testing frameworks.Integrate automated testing into CI/CD pipelines to enable smoke, regression, nightly, and release validation.Validate computer vision models and pipelines, including image/video ingestion, object detection, classification, tracking, and event/alert generation.Design, curate, and maintain test datasets and benchmark scenarios to evaluate model accuracy, performance, robustness, and edge cases.Conduct AI model validation and regression testing using performance metrics, baselines, and real-world datasets.Verify the reliability and consistency of GenAI-powered capabilities, including prompt workflows, reasoning, and AI-assisted automation features.Perform performance, scalability, and reliability testing for real-time Vision AI services, ensuring required throughput, latency, and system stability.Lead security testing activities, including vulnerability assessment, threat validation, adversarial testing, and secure deployment verification for AI applications.Drive defect triage, root-cause analysis, quality reporting, and continuous improvement initiatives in collaboration with engineering, product, and DevOps teams.Ensure Vision AI solutions meet quality, security, compliance, and production-readiness standards before release.

Skill Requirements

Strong foundation in Quality Assurance principles, including test strategy, test planning, test design, defect management, and root-cause analysis.Hands-on experience in test automation using Python and industry-standard frameworks such as Selenium, Cypress, or equivalent automation tools.Proven experience testing web applications, APIs, and microservices, including REST API validation, authentication workflows, and payload verification using tools such as Postman.Good understanding of databases and SQL, with the ability to query and validate data across application and AI workflows.Experience integrating automated test suites into CI/CD pipelines and supporting continuous quality validation.Strong understanding of AI/ML systems and methodologies for validating model accuracy, reliability, and performance.Experience testing GenAI and LLM-based applications, including prompt engineering workflows, multi-turn conversations, tool/function calling, and response evaluation.Knowledge of hallucination detection, grounding validation, response quality assessment, and safety testing for AI applications.Understanding of computer vision solutions and video analytics, including validation of object detection, classification, tracking, and common AI performance metrics such as Precision, Recall, F1-Score, and Accuracy.Experience designing and executing security testing using tools and methodologies such as OWASP ZAP, application security assessments, vulnerability validation, and secure SDLC practices.Hands-on experience with performance, scalability, and load testing using tools such as JMeter, LoadRunner, or equivalent, ensuring AI platforms meet performance and reliability targets.Familiarity with cloud-native applications, containerized environments, and modern software development and DevOps practices is preferred.Strong analytical, troubleshooting, communication, and stakeholder collaboration skills.

Other Requirements

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