Software Development Engineer in Test (SDET)

Software Development Engineer in Test (SDET)
Agentic AI Automation | Playwright / Selenium / Mabl | Pega & Performance Engineering
📍 Location: Hyderabad, India
💼 Experience: 3-7 Years
🧑‍💻 Employment Type: Full-Time / Contract
🏢 Company: NForce One Pvt Ltd.
About NForce One
NForce One is a technology services and solutions company with delivery capabilities across the United States and India.
We build AI-powered products, enterprise applications, accelerators, and client-specific solutions across Generative AI, Agentic AI, Quality Engineering, Data Engineering, Cloud, and Digital Transformation.
Role Overview
We are looking for a hands-on Software Development Engineer in Test (SDET) with strong, demonstrable experience in Agentic AI automation.
Agentic AI experience is a core requirement for this role. We are looking for someone who has already applied agentic AI to real-world testing problems and can demonstrate their work, such as:
Autonomous test agents
AI-driven test case generation
Self-healing automation
Intelligent test selection
Automated failure triage
Root cause analysis
Candidates whose AI exposure is limited to occasional chatbot usage will not be a fit.
You should have designed, built, or integrated agent-based tooling into a working test pipeline and be able to explain the architecture, prompts and context strategy, guardrails, and measurable impact on cycle time and defect detection.
You will also be responsible for testing AI itself, including LLM-as-a-Judge evaluation for AI chatbots and agents.
Alongside AI, you should be a strong software engineer with hands-on experience designing and owning automation frameworks across web, API, and Pega-based enterprise applications.
Our teams work across multiple automation stacks, including Playwright, Selenium, and Mabl. You should be comfortable working across different frameworks, supporting teams on a stack that may not be your primary one, and helping evaluate or consolidate tooling where appropriate.
This is a coding and engineering role, not a manual testing or test-management-only position.
Key Responsibilities
Design, build, and maintain scalable test automation frameworks using Playwright and Selenium for web, cross-browser, and end-to-end regression testing.
Design and execute LLM-as-a-Judge evaluation suites for AI chatbots and agents, including rubric definition, golden datasets, automated scoring, regression tracking, and reporting.
Evaluate AI systems for hallucination, groundedness, relevance, safety, toxicity, and prompt injection.
Build and maintain low-code UI automation in Mabl, including test creation, auto-healing configuration, coverage review, and failure triage.
Automate functional and regression testing for Pega applications, including case lifecycle flows, work objects, portals, and dynamically generated UI components.
Build and integrate Agentic AI capabilities into the testing lifecycle, including:
AI-assisted test case generation
Self-healing locators
Intelligent test selection
Automated failure triage
Root cause analysis
Develop and maintain API automation for REST and SOAP services using tools such as Postman, ReadyAPI, SoapUI, REST Assured, or Playwright API testing.
Implement contract validation, test data setup, and service virtualization.
Design, script, execute, and analyze performance and load tests using NeoLoad, LoadRunner, or JMeter.
Analyze performance bottlenecks, response-time trends, and capacity risks.
Own test automation CI/CD pipelines, including parallel and cross-browser execution, containerized test runs, environment provisioning, and consolidated reporting.
Define test strategies for new features, including risk analysis, coverage targets, test data strategy, and entry/exit criteria.
Perform code reviews for application and test code and advocate for testability in design and architecture discussions.
Investigate failures, isolate defects, and drive issues to closure with clear, reproducible evidence.
Track and publish quality metrics including automation coverage, flakiness, escaped defects, and mean time to detection.
Build reusable automation components and accelerators.
Evaluate emerging AI and testing frameworks for adoption across accounts.
Share automation practices, coding standards, and modern AI-assisted testing workflows with the wider QA team.
Required Qualifications
3+ years of professional experience in software testing, with at least 2 years in a hands-on SDET or test automation engineering role.
Proven hands-on experience with Agentic AI automation in a testing context, with specific examples that can be explained in detail.
Experience building or integrating autonomous or multi-agent test workflows, AI-driven test case generation, self-healing automation, intelligent test selection, or automated defect triage/root cause analysis.
Working knowledge of LLM integration fundamentals, including:
Prompt and context engineering
Structured outputs
Tool/function calling
Guardrails
Agent reliability evaluation
Hands-on experience with LLM-as-a-Judge evaluation testing for AI chatbots and agents.
Experience with rubric and scoring design, golden datasets, automated regression evaluation, and testing for hallucination, groundedness, relevance, toxicity, and prompt injection.
Strong programming skills in at least one of:
Java
TypeScript / JavaScript
Python
C#
Production-level UI automation experience with at least two of:
Playwright
Selenium WebDriver
Mabl
Strong depth in Playwright or Selenium framework design.
Hands-on experience testing Pega applications, including Pega Platform 8.x or Pega Infinity.
Understanding of Pega case management, rules, flows, sections, and UI model.
Experience automating Pega UI, including dynamic DOM structures, iframes, shadow elements, and unstable locators.
Hands-on API testing experience with Postman, ReadyAPI, SoapUI, or equivalent tools, covering REST and SOAP services.
Hands-on performance testing experience with NeoLoad, LoadRunner, or JMeter, including scripting, workload modeling, execution, and results analysis.
Experience with CI/CD tools such as:
Jenkins
GitHub Actions
GitLab CI
Azure DevOps
Strong SQL skills and experience validating data across relational databases.
Proficiency with Git, branching strategies, and code review practices.
Strong software engineering fundamentals.
Excellent written and verbal communication skills with the ability to explain quality risks to technical and business stakeholders.
Preferred Qualifications
Strong experience with Mabl, including journey design, auto-healing, data-driven runs, CI integration, reporting, and coverage analysis.
Experience migrating or consolidating automation suites between frameworks, such as Selenium to Playwright or scripted automation to low-code platforms.
Pega certification such as:
CSA – Certified System Architect
CSSA – Certified Senior System Architect
Experience with Pega-native testing capabilities:
Pega Scenario Testing
PegaUnit
Pega Deployment Manager
Experience with LangChain, LangGraph, CrewAI, AutoGen, or MCP-based agent integrations applied to quality engineering.
BDD experience with Cucumber, SpecFlow, or Playwright with Cucumber.
Containerization/orchestration experience with Docker and Kubernetes.
Hands-on experience with at least one major cloud platform:
Azure
AWS
GCP
Experience with test management and reporting tools such as Jira, Zephyr, Xray, Allure, or ReportPortal.
Mobile automation experience with Appium.
Experience in regulated domains such as banking, financial services, insurance, or healthcare, where Pega is commonly deployed.
Nice to Have
Accessibility testing (WCAG 2.1 AA)
Security testing fundamentals and OWASP awareness
Chaos and resiliency testing
Contract testing with Pact
Observability tools such as Grafana, Dynatrace, Datadog, or AppDynamics
Shift-left testing and test data management strategies
Experience building QA accelerators or reusable frameworks for multiple clients
Ideal Candidate Profile
You are a hands-on automation engineer who writes clean, maintainable code and thinks in terms of risk rather than test counts.
You are equally comfortable:
Debugging a flaky Pega locator
Troubleshooting a failing API contract
Analyzing a performance test that does not scale
Building AI-powered testing workflows
Evaluating AI agents and LLM-based applications
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Work arrangement
No