Experienced QA Professional with AI Skills

QA Engineer -AI & Engineering

Join our AI & Engineering team in transforming technology platforms, driving innovation, and helping make a significant impact on our clients' success. You’ll work alongside talented professionals reimagining and re-engineering operations and processes that are critical to business. Your contributions can help clients improve financial performance, accelerate new digital ventures, and fuel growth through innovation.

AI & Engineering leverages cutting-edge engineering capabilities to build, deploy, and operate integrated/verticalized sector solutions in software, data, AI, network, and hybrid cloud infrastructure. These solutions are powered by engineering for business advantage, transforming mission-critical operations. We enable clients to stay ahead with the latest advancements by transforming engineering teams and modernizing technology & data platforms. Our delivery models are tailored to meet each client's unique requirements.

Engineering as a Service provides complete design, implementation, and technology operations, leveraging our core engineering expertise. We transform engineering teams, modernize technology, and deliver complex programs with a product engineering approach. Our flexible delivery models—traditional teams, pools, or pods—are tailored to each client’s needs, offering engineering-led advisory, implementation, and operational capabilities to accelerate innovation.

Work You'll Do

As an SDET II, you will bring strong technical expertise to deliver effective solutions to clients including the following:

• Working as part of the SDET team, primarily in an individual contributor role.
• Helping to create testing solutions to meet our clients’ business needs.
• Leading the testing of custom technology solutions.

Required Qualifications and Experience

• 4+ years’ relevant consulting or industry experience.
• Bachelor’s degree in computer science, engineering or a related field.
• 2+ years hands-on experience defining the testing strategy for large scale programs and ensuring requirements traceability throughout the system development lifecycle. Experience should include leading the testing efforts, including experience with all phases of testing: system integration, user acceptance, and performance testing.
• Experience with at least two of the following: TCOE, TMO, mobile testing, automation testing, SAP/Oracle testing, data testing in a client-facing or industry role.
• 4+ years’ experience driving the quality assurance processes and gaining delivery and buy-in from executive leadership.
• Experience with Test and Defect Management tools – MicroFocus, ALM, JIRA.
• 2+ years’ experience working in a testing role in different environments including the following: client server, service-oriented architecture, web-based, mainframe-based and three-tier architectures.
• Experience with the following: MicroFocus UFT, Rational Tooling, and Open Source Automation Tools such as Selenium, Cucumber, OpSoapUI, PostMan, RESTAssured, Mockito.
• Experience in at least one Scripting language – Shell, VBScript, Perl or Python.
• Experience with Source Control Systems – SVN or GIT.
• Experience with Build tools – Maven, CircleCI.
• Experience with Automated Reporting.
• Experience with Cloud testing, API testing
• Experience with CI/CD/CT tools – Jenkins, Bamboo, Quickbuild.
• Experience applying prompt engineering techniques to generate, refine, or debug automated test scripts and test data.

• Ability to travel up to 50% of the time, based on the work you do and the clients and industries/sectors you serve.
• Limited immigration sponsorship may be available.

Preferred Qualifications and Experience

• Experience working in Cloud, Web and Mobile Testing platforms – Saucelabs, Browerstack are preferred.
• Hands-on experience with machine learning models.
• Experience in Python NLTK or similar natural language processing technology.
• Experience evaluating or testing AI/LLM-based application features (e.g., chatbots, copilots, RAG-based systems), including prompt-response validation and hallucination/edge-case testing.
• Exposure to AI-assisted defect triage or root-cause analysis tools that reduce manual investigation time.
• Exposure to self-healing test automation frameworks (AI-driven locator repair, visual AI testing).
• Understanding of responsible AI/testing governance principles (bias detection, explainability, model validation) as applied to QE.
• Experience contributing to or influencing AI-adoption roadmaps within a testing/QE organization.
• Experience implementing testing best practices specific to the test planning and execution processes and gaining buy-in for changes from executives.
• A deep understanding of testing planning and associated delivery challenges.
• A strong understanding of the various approaches used in testing techniques – white-box, black-box, load testing, coverage testing and regression testing.