Quality Assurance Engineer ensuring compliance across ground segment systems at CROWDCONSULTANTS. Monitoring QA activities and supporting engineering lifecycle in highly regulated space environment.
About the role
- Technical Lead in Quality Assurance leading QA initiatives across software and next-gen AI. Responsible for QA strategy, team mentoring, and process improvement.
Responsibilities
- Define and execute QA strategy across software products including GenAI agents, LLM-based tools, APIs, and web/mobile applications.
- Establish and monitor quality goals, test coverage, release readiness, and risk-based validation plans for AI and traditional systems.
- Drive test architecture and frameworks for both functional and AI-specific evaluations.
- Ensure best practices in STLC, SDLC, Agile methodologies, and CI/CD processes.
- Maintain and report test execution metrics, defect leakage, test effectiveness, and quality KPIs.
- Collaborate with cross-functional stakeholders across engineering, product, and data science teams to align quality goals.
- Manage, mentor, and grow a team of QA engineers, automation experts, and analysts.
- Conduct performance reviews, skill-gap analysis, and career planning for team members.
- Support recruitment, onboarding, and scaling of the QA team.
- Foster a culture of continuous improvement, innovation, accountability, and learning.
- Facilitate effective internal and external communication regarding project updates, risks, and timelines.
- Lead development of test automation strategies using tools such as Selenium, Playwright, Cypress, Robot Framework.
- Integrate automation into CI/CD pipelines using tools like Jenkins, GitHub Actions, etc.
- Design and maintain performance testing frameworks using tools such as JMeter, LoadRunner, and SoapUI.
- Incorporate GenAI-specific test tools like DeepEval, PromptLayer, and LLM observability tools into automation pipelines.
- Apply basic scripting knowledge (Python/Java) for framework customization and tool integration.
- Design robust evaluation frameworks for GenAI/LLM systems including: Output accuracy, hallucination detection, factual consistency.
- Prompt testing, adversarial scenarios, and edge case validation.
- Collaborate with AI/ML teams to define evaluation hooks, model behaviors, benchmarking standards, and observability.
- Use AI to generate and maintain test scenarios for intelligent systems.
Requirements
- Bachelor’s/Master’s degree in Computer Science, Engineering, or related field.
- 10+ years of QA experience with at least 4+ years in a leadership/managerial capacity.
- Proven experience in building and scaling automation frameworks and QA practices.
- Hands-on experience with Generative AI model behaviors (e.g., GPT-4, Claude, RAG pipelines, LLMs).
- Strong understanding of Agile/Scrum methodologies, DevOps pipelines, CI/CD processes.
- Familiarity with test management tools and requirement traceability processes.
- Ability to interpret and assess software requirements, perform impact analysis, and contribute to high-quality test design.
- Deep understanding of quality assurance artifacts including test plans, test cases, test data, and defect life cycle.
Benefits
- This role offers a unique opportunity to be at the forefront of software quality assurance — combining traditional QA excellence with AI-driven innovation. If you're passionate about quality and future-facing technologies, we'd love to hear from you.
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