Tilak Nagar Delhi Delhi Senior quality engineer (ai augmented testing) Jobs – 17 Open Jobs Today

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Senior Quality Engineer (AI-Augmented Testing)

Lister Digital
Gurugram, Haryana

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Job details

Full-time
5 days ago

Qualifications

Jira
CI/CD
Fiddler
ISTQB Certification
Azure
Law
Gherkin (programming language)
Cucumber (software testing tool)
DevOps
Test automation
Selenium
Git
Master's degree
Microservices
AWS
Quality assurance
Bachelor's degree
System testing
Continuous integration
REST
Scripting
GitHub
APIs
Agile
Financial services
GraphQL
AI
gRPC
Jenkins
GitLab
Communication skills
Python
Spring framework

Full job description

About This Role

6 to 10 years of experience
Skills: Test Automation, Selenium, Playwright, AI Testing, LLM Testing, RAG Pipeline Testing, Python, REST API Testing, RAGAS / DeepEval / LangSmith, CI/CD, BDD / Cucumber, Prompt Testing, Hallucination Detection, Shift-Left Testing, JIRA

About the Role
We are seeking a Senior Quality Engineer with 8–10 years of experience and strong AI / GenAI testing expertise to lead and elevate quality engineering practices across enterprise applications and AI-powered platforms. You will bring deep test automation skills combined with hands-on experience validating LLM outputs, RAG pipelines, GenAI applications, and ML model performance — ensuring that AI systems are accurate, reliable, fair, and safe for production use. Beyond AI testing, you will champion shift-left quality practices, design robust automation frameworks, and embed quality as a first-class concern across Agile delivery teams. This is a high-impact role at the cutting edge of quality engineering — where traditional QE rigour meets the unique and evolving challenges of AI system validation.

Key Responsibilities

AI & GenAI Quality Assurance

Design and implement comprehensive testing strategies for GenAI applications — covering LLM output validation, RAG pipeline testing, prompt regression, hallucination detection, and responsible AI evaluation.
Build and maintain LLM evaluation frameworks using RAGAS, TruLens, DeepEval, or LangSmith — defining and tracking metrics for answer relevance, faithfulness, context precision, and recall across RAG and generative AI systems.
Design prompt testing frameworks — systematically validating prompt behaviour, regression testing prompt changes, and assessing LLM output consistency and quality across model versions.
Conduct adversarial testing and red-teaming of AI systems — probing for jailbreaks, prompt injection vulnerabilities, bias, and safety filter bypasses to ensure AI outputs meet safety and compliance standards.
Validate AI guardrails and safety filters — testing content moderation, output filtering, and responsible AI controls across GenAI applications.
Test for model drift and performance degradation — implementing monitoring-based quality checks that detect and alert on changes in AI model behaviour over time in production.
Develop synthetic test data generation pipelines using LLMs — producing diverse, representative, and edge-case-rich test datasets for AI and non-AI system testing.
Collaborate with data scientists and ML engineers to validate ML pipeline quality — testing data ingestion, feature engineering, model training outputs, and inference APIs across the ML lifecycle.

Test Automation & Framework Development

Design, build, and maintain scalable, maintainable test automation frameworks using Selenium, Playwright, or equivalent — supporting functional, regression, and end-to-end testing across web and API layers.
Develop API test automation suites using RestAssured, Karate, or Postman — covering REST, GraphQL, and gRPC service contracts with comprehensive positive, negative, and boundary test coverage.
Implement AI-powered test automation — leveraging tools such as Testim, Mabl, or Applitools for self-healing, visual AI, and intelligent test case generation and maintenance.
Build and maintain BDD test frameworks using Cucumber and Gherkin — collaborating with product owners and business analysts to define clear, business-readable acceptance criteria and test scenarios.
Design and execute performance and load testing strategies using JMeter, Gatling, k6, or Locust — validating scalability, throughput, and latency under realistic and peak load conditions.

Shift-Left & CI/CD Quality Integration

Embed quality gates and automated test suites into CI/CD pipelines — ensuring every code change is validated through fast, reliable, and comprehensive automated checks before reaching production.
Champion shift-left testing practices — engaging with development, architecture, and product teams early in the delivery lifecycle to define testability requirements and prevent defects at source.
Implement contract testing using Pact or Spring Cloud Contract — validating microservices API contracts and preventing integration failures across distributed service boundaries.
Configure and maintain test reporting and observability — Allure, ReportPortal, or ExtentReports — providing clear, actionable quality metrics and trend analysis for engineering and stakeholder audiences.
Support shift-right testing practices — implementing production monitoring, synthetic monitoring, and canary testing strategies to detect and respond to quality issues in live environments.

Quality Leadership & Collaboration

Define and own the quality engineering strategy across assigned programmes — establishing test approaches, automation roadmaps, and quality metrics aligned to programme goals and risk profiles.
Lead test planning, estimation, and risk-based prioritisation — ensuring test coverage is comprehensive, well-targeted, and proportionate to business and technical risk.
Conduct and facilitate test design workshops — working with development, BA, and product teams to derive thorough test scenarios from requirements, user stories, and acceptance criteria.
Manage defect triage, root cause analysis, and quality reporting — providing clear, evidence-based quality status updates to delivery teams and senior stakeholders.
Contribute to and promote a Quality Engineering Centre of Excellence (CoE) — developing reusable frameworks, shared tooling, best practice guidelines, and training resources for the broader QE community.
Mentor and coach junior and mid-level quality engineers — conducting knowledge-sharing sessions, reviewing test code, and elevating QE capability across the team.

What We're Looking For

8–10 years of quality engineering experience with a strong and demonstrable track record across both traditional test automation and AI / GenAI system testing.

Proven hands-on experience testing LLM-powered applications and RAG pipelines — including output validation, hallucination detection, prompt regression, and responsible AI evaluation.

Strong test automation expertise — Selenium, Playwright, or equivalent — with experience designing and building scalable, maintainable automation frameworks from scratch.

Solid API testing skills — REST, GraphQL, and gRPC — using RestAssured, Karate, Postman, or equivalent tools in production testing environments.

Hands-on experience with LLM evaluation frameworks — RAGAS, TruLens, DeepEval, LangSmith, or equivalent — for structured AI output quality measurement.

Advanced Python skills — for AI testing, automation framework development, test data generation, and LLM evaluation scripting.

Strong CI/CD integration experience — embedding quality gates, automated test suites, and reporting into Jenkins, GitLab CI, GitHub Actions, or Azure DevOps pipelines.

Experience testing cloud-native and microservices architectures on AWS, Azure, or GCP — including contract testing, distributed system validation, and container workload testing.

Deep understanding of shift-left quality practices — engaging early in the delivery lifecycle to define testability, acceptance criteria, and quality requirements.

Excellent communication skills — able to present quality metrics, risk assessments, and AI testing findings clearly to both technical and non-technical stakeholders.

Nice to Have

Experience with chaos engineering and resilience testing — LitmusChaos, Gremlin, or equivalent — for validating AI system fault tolerance and recovery.
Familiarity with multimodal AI testing — validating text, image, audio, and video AI model outputs.
Knowledge of AI red-teaming methodologies and adversarial ML attack patterns.
Exposure to MLOps platforms — MLflow, Kubeflow, or SageMaker — for ML pipeline quality validation and model governance testing.
Experience with vector database validation — OpenSearch, Pinecone, FAISS — for RAG retrieval quality testing.
Familiarity with AI observability platforms — Arize AI, WhyLabs, or Fiddler — for production AI model monitoring and quality tracking.
ISTQB Advanced Level — Test Automation Engineer or Test Manager certification.
ISTQB CT-AI — Certified Tester AI Testing certification.
AWS / Azure / GCP cloud fundamentals or associate-level certification.
Experience in Financial Services, HealthTech, or other regulated industry environments where AI governance and explainability are critical.

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