Kannan Balakrishnan

About Kannan Balakrishnan

Senior SDET with 8+ years of experience in automation engineering, AI/LLM quality, and end-to-end testing of large-scale enterprise and GenAI-driven applications. I design robust automation frameworks, validate complex platform products, and drive quality strategy across Web, API, Mobile, and AI systems.Skilled in Java, JavaScript, Python, Playwright, Selenium, WebDriver, TestNG/JUnit, REST-Assured, Appium, Jenkins, Docker, Cucumber (BDD), TDD, CI/CD, Salesforce testing, SRE practices, and data-driven/keyword-driven frameworks.Currently owning quality for a ServiceNow product—defining test strategy, building automation pipelines, and partnering with PM, UX, Architects, and Engineering to deliver secure, scalable, and high-quality releases.AI/LLM, RAG & Agent Testing Expertise: • LLM output evaluation, hallucination detection, prompt behavior analysis • RAG testing (retrieval quality, grounding, chunking, latency, context accuracy) • AI Agents testing (reasoning validation, workflow correctness, action safety) • GenAI workflow QA, scoring engines, dynamic configurationsPassionate about engineering excellence, test automation at scale, quality governance, performance/security readiness, and improving developer productivity. I enjoy mentoring teams, leveling up testing standards, and contributing to product and architecture decisions with a strong quality mindset.

Experience

1. Senior Software Qe Engineer

   ServiceNow

   Aug 2023 — Present

   Test Leadership & Quality Strategy• Led end-to-end quality strategy for the Smart Assessment Engine, a configurable framework across multiple ServiceNow product lines.• Defined scalable test architecture (unit, integration, API, UI, E2E) ensuring strong coverage for dynamic, rules-driven products.• Drove automation-first practices, built regression suites, and integrated quality gates into CI/CD pipelines.• Partnered with PM, UX, and Engineering to refine requirements, perform risk analysis, and embed quality early in SDLC. AI, LLM & Gen-AI Validation• Directed evaluation/testing of GenAI features: prompt workflows, model outputs, grounding logic, hallucination checks, and data relevance.• Performed RAG pipeline validation (retrieval quality, chunking, context injection, grounding, latency accuracy).• Verified Agent/AI automation behavior for safe, predictable execution.• Defined quality benchmarks for AI features—accuracy, reliability, interpretability, and safety. Product Delivery & Collaboration• Ensured release readiness for major launches, validating functionality, performance, usability, and reliability under tight timelines.• Partnered with architects, managers, and cross-BU teams to deliver consistent quality across platform features.• Influenced product roadmap by highlighting quality risks, test impacts, and scalability considerations. Quality Governance & Best Practices• Performed secure testing with Security Champions—integrating static scans, vulnerability triage, and secure coding checks.• Improved efficiency via test data management, optimized automation coverage, and reduced regression cycles.• Mentored engineers on testing best practices, automation patterns, and debugging techniques.

Education

• Madha Engineering College

  Bachelor of Engineering - BE, Electronics and Communications Engineering