Hi, I’m Sulabh Gupta. I’m a Senior Quality Engineer with 13+ years of experience delivering scalable, CI-driven testing solutions across Web, Mobile, API, and Performance testing in Agile and DevOps environments. I design, develop, and scale UI and API automation frameworks and am a strong advocate of shift-left testing, quality gates, code reviews, and reliability engineering.
Hello! I’ve led QA efforts for enterprise fintech projects, built reusable automation components, and continuously improve test stability using AI-assisted tooling to accelerate development and reduce flaky tests. I enjoy collaborating with cross-functional teams to ship high-quality software quickly.
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An open-source AI testing portfolio project that evaluates a RAG-based trade surveillance agent for detecting wash trading, layering, and spoofing in Capital Markets. Built end-to-end in Python with a fully automated CI/CD evaluation pipeline.
● Designed and implemented a Python-based LLM evaluation framework using DeepEval metrics (AnswerRelevancyMetric, FaithfulnessMetric, ContextualPrecisionMetric, GEval) to assess RAG pipeline quality for financial compliance use cases.
● Built RAGAS evaluation suite to measure retrieval augmentation quality across trade surveillance query scenarios including context recall, answer correctness, and context relevance.
● Integrated evaluation pipeline into GitHub Actions CI/CD; automated DeepEval and RAGAS test runs on each commit with threshold-based quality gates and custom HTML reporting.
● Stack: Python 3, pytest, DeepEval, RAGAS, Ollama (local), OpenAI GPT-4o, GitHub Actions | Domain: Capital Markets, AML, Wash Trading, Spoofing, MiFID II
Git Repo: https://www.twine.net/signin
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