{"id":3900029,"url":"https://alion.io/job/iderize-ai-quality-engineer","title":"AI Quality Engineer","company":{"id":3800771,"name":"Iderize","domain":"techjays.com","url":"https://alion.io/company/iderize","size_band":"51-200","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":null,"truth_index":null},"role":"AI/ML","role_family":"AI/ML","seniority":"junior","employment_type":"full_time","work_mode":"hybrid","remote_scope":null,"remote_scope_basis":null,"remote_working_hours":null,"hiring_geo_confidence":"structured","locations":["Coimbatore, India"],"countries":["IN"],"hiring_countries":[],"hiring_countries_total":0,"salary":null,"salary_estimate":null,"experience_years_min":2,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"LLM","optional":false},{"name":"Agile","optional":true},{"name":"AI Agents","optional":true},{"name":"Anthropic","optional":true},{"name":"BrowserStack","optional":true},{"name":"CI/CD","optional":true},{"name":"Claude","optional":true},{"name":"Confluence","optional":true},{"name":"Cucumber","optional":true},{"name":"Cypress","optional":true},{"name":"DeepEval","optional":true},{"name":"JavaScript","optional":true},{"name":"LangChain","optional":true},{"name":"LangGraph","optional":true},{"name":"LlamaIndex","optional":true},{"name":"Machine Learning","optional":true},{"name":"Playwright","optional":true},{"name":"Postman","optional":true},{"name":"Prompt Engineering","optional":true},{"name":"Python","optional":true},{"name":"RAG","optional":true},{"name":"Ragas","optional":true},{"name":"Rest API","optional":true},{"name":"Scrum","optional":true},{"name":"Selenium","optional":true},{"name":"Semantic Search","optional":true},{"name":"SQL","optional":true}],"status":"live","first_seen_at":"2026-11-06T13:14:41Z","employer_posted_date":null,"last_verified_at":"2026-11-06T13:14:41Z","board_verified":false,"closed_at":null,"days_open":0,"trust":{"level":"not_scored","repost_count":null,"flags":[],"days_open":0},"description":"TECHJAYS\nTHE AI REIMAGINATION COMPANY\nAI Quality Engineer\n\nPosition : AI Quality Engineer\nExperience : 3–10 Years\nLocation : Coimbatore / Hybrid\nJoining : Immediate / Within 30 Days\n\nPrimary SkillsManual & Functional Testing, AI/ML Application Testing, LLM Testing, Prompt Testing, Test Case Design, API Testing, Regression Testing, Defect Management\nPreferred SkillsRAG Testing, Chatbot Testing, AI Response Evaluation, Selenium, Playwright, Cypress, Python, JavaScript, SQL, Postman, JIRA, BDD\n\nAbout Techjays\nAt Techjays, we're helping businesses reimagine how they build, operate, and scale with AI at the core. We focus on developing intelligent, scalable, and production-grade AI solutions that solve real-world business challenges.\nOur global team brings together experienced technology professionals from organizations such as Google, NetApp, ADP, Cognizant Consulting, and Capgemini. We work with global clients to build AI-powered applications, intelligent automation platforms, and enterprise software solutions.\nWe're looking for an AI Quality Engineer with strong functional testing expertise and prior hands-on experience testing AI/ML-powered applications.\nThis role goes beyond traditional software testing. You will be responsible for validating the quality, accuracy, reliability, and behaviour of AI-powered features, ensuring that intelligent applications perform consistently across different scenarios and real-world use cases.\nThe ideal candidate should understand both traditional QA methodologies and the unique testing challenges associated with AI systems, including non-deterministic outputs, prompt behaviour, hallucinations, response relevance, and edge-case validation.\nPrior hands-on experience testing AI/ML-based products or applications is mandatory. Candidates with only traditional manual testing experience and no AI project exposure will not be a fit.\nKey Responsibilities\n1. AI Application & LLM Testing\nPerform functional and quality testing of AI-powered applications, LLM-based products, chatbots, and intelligent automation systems.\nDesign test scenarios to validate AI-generated responses for accuracy, relevance, consistency, completeness, and contextual understanding.\nTest prompts, prompt variations, and conversational flows to identify unexpected outputs and behavioural inconsistencies.\nEvaluate AI responses for hallucinations, incorrect information, irrelevant answers, and context retention issues.\nValidate AI application behaviour across different user inputs, edge cases, and complex scenarios.\nTest RAG-based applications by validating document retrieval, response relevance, contextual accuracy, and source grounding.\nEvaluate AI-powered search, recommendation engines, and other intelligent product features.\nCollaborate with AI engineers and data science teams to define acceptance criteria and quality benchmarks.\n2. Functional & Manual Testing\nAnalyse business and technical requirements and convert them into comprehensive test scenarios and test cases.\nExecute functional, regression, integration, system, and user acceptance testing across web and mobile applications.\nValidate end-to-end application workflows, business logic, user interactions, and data processing.\nIdentify functional gaps, unexpected behaviour, and usability issues.\nPerform exploratory testing to uncover defects that may not be covered by predefined test cases.\nEnsure application functionality remains stable across different releases and environments.\nReview requirements early in the development lifecycle to identify potential quality risks.\n3. AI Quality Evaluation & Validation\nDevelop structured testing approaches for non-deterministic AI outputs using behaviour-based, range-based, and scenario-based validation.\nEvaluate AI response quality against expected outcomes and predefined acceptance criteria.\nValidate prompt sensitivity, context handling, conversation memory, and multi-turn interactions.\nIdentify failure patterns involving hallucinations, prompt injection, incorrect retrieval, and inconsistent responses.\nTest AI system performance across different input formats, languages, and user scenarios where applicable.\nMaintain test datasets and evaluation scenarios for repeatable AI quality assessment.\nWork with engineering teams to identify root causes of AI-related defects and verify fixes.\n4. API & Integration Testing\nPerform manual API testing using Postman or similar tools.\nValidate API request and response structures, status codes, authentication, authorization, and error handling.\nTest integrations between AI services, backend systems, databases, and external APIs.\nValidate data flow between application components and identify integration issues.\nTest API behaviour under different input conditions, including invalid inputs and boundary cases.\nVerify API responses and ensure consistency between frontend and backend functionality.\n5. Defect Management & Quality Reporting\nIdentify, document, track, and manage defects throughout the testing lifecycle.\nMaintain detailed test cases, test execution reports, defect logs, and test documentation.\nCollaborate with developers to reproduce issues, analyse root causes, and validate fixes.\nTrack test coverage, defect trends, and application quality metrics.\nCommunicate release risks, unresolved defects, and testing status to relevant stakeholders.\nParticipate in release readiness reviews and provide quality sign-off for assigned modules.\n6. Automation & Continuous Improvement\nIdentify repetitive test scenarios that can be automated to improve testing efficiency.\nSupport automation initiatives using Selenium, Playwright, or Cypress.\nWork with developers to improve testability and integrate testing practices into CI/CD workflows.\nExplore AI-assisted testing tools and approaches to improve test coverage and efficiency.\nContinuously improve testing methodologies for both conventional software applications and AI-powered systems.\nRequired Skills & Experience\n1. Functional & Manual QA\n3–5 years of hands-on experience in manual and functional software testing.\nStrong understanding of software testing methodologies, SDLC, STLC, and defect management.\nExperience designing and executing test cases for functional, regression, integration, and end-to-end testing.\nStrong knowledge of Agile/Scrum methodologies and sprint-based QA processes.\nExperience testing web applications, APIs, and mobile applications.\nAbility to analyse requirements and identify functional gaps, edge cases, and potential risks.\n2. Mandatory AI/ML Testing Experience\nPrior hands-on experience testing AI/ML-powered applications or products.\nPractical experience testing LLM-based applications, AI chatbots, conversational systems, or intelligent automation features.\nUnderstanding of prompt testing and evaluating AI-generated responses.\nAbility to validate AI outputs for accuracy, relevance, consistency, and contextual correctness.\nExperience identifying hallucinations, unexpected AI behaviour, and response-quality issues.\nUnderstanding of AI application workflows, model outputs, and the challenges associated with non-deterministic systems.\nExperience collaborating with AI/ML engineers or data science teams during testing and validation.\n3. API & Integration Testing\nHands-on experience with manual API testing using Postman.\nUnderstanding of REST APIs, JSON, request/response validation, authentication, and error handling.\nExperience validating integrations between frontend, backend, and external services.\nBasic understanding of database validation and SQL queries.\n4. Testing Tools & Technologies\nExperience with JIRA and Confluence for defect tracking and test documentation.\nFamiliarity with Postman, SQL, and browser developer tools.\nUnderstanding of test management and reporting practices.\nBasic knowledge of BDD frameworks such as Cucumber/Gherkin is an advantage.\nPreferred Skills\nExperience testing RAG-based applications, including document retrieval and contextual response validation.\nExposure to vector databases and semantic search testing.\nFamiliarity with LangChain, LangGraph, LlamaIndex, or similar AI frameworks.\nUnderstanding of LLM evaluation frameworks and AI quality assessment methodologies.\nExperience with AI evaluation tools such as Ragas, DeepEval, or similar frameworks.\nExposure to prompt injection, adversarial testing, and responsible AI validation.\nWorking knowledge of Selenium, Playwright, or Cypress.\nBasic scripting knowledge in Python or JavaScript.\nFamiliarity with CI/CD pipelines and automated testing practices.\nExposure to cross-browser testing tools such as BrowserStack or Sauce Labs.\nWhat We're Looking For Beyond Skills\nAI Testing Mindset: Understands that testing AI applications requires more than checking whether a feature works. Can evaluate response quality, accuracy, relevance, and behaviour.\nAnalytical Thinking: Ability to identify unusual scenarios, inconsistent outputs, and hidden quality issues.\nStrong QA Fundamentals: Applies structured testing methodologies while adapting them to AI-powered applications.\nAttention to Detail: Documents defects clearly and identifies patterns in application and AI behaviour.\nOwnership: Takes responsibility for testing quality from requirement analysis through release validation.\nCollaboration: Works effectively with developers, AI engineers, product managers, and data science teams.\nContinuous Learning: Interested in exploring emerging AI technologies and evolving testing methodologies.\nCommunication: Clearly communicates test results, defect impact, quality risks, and release readiness.\nWhat We Offer\nCompetitive compensation aligned with experience and expertise.\nFlexible work environment (Coimbatore office / Hybrid).\nPaid holidays and flexible time off.\nMedical insurance covering self and family, up to ₹4 lakh per person.\nOpportunity to work on real-world AI-powered products and enterprise applications.\nHands-on exposure to LLMs, RAG systems, agentic AI, and modern AI engineering practices.\nOpportunities to develop expertise in AI quality engineering and automation testing.\nA collaborative, multicultural environment with opportunities for professional growth.\nA culture that values clarity, integrity, ownership, and continuous learning.\nJoin Techjays\nIf you're passionate about software quality and have hands-on experience validating AI-powered applications, we'd love to hear from you.\nJoin us in building reliable, intelligent, and production-ready AI systems.\nSkills\nSoftware Testing (QA), Artificial Intelligence (AI), Anthropic Claude, Prompt engineering, Machine Learning (ML), Selenium, Retrieval Augmented Generation 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