LLM Evaluation Systems
Designs repeatable eval harnesses for accuracy, refusal behavior, tool use, regressions, and multi-turn reasoning quality.
Open source + enterprise quality engineering
Srihari NaiduEngineering Quality for AI Systems
Lead SDET | AI Quality Engineer | Automation Architect
Lead SDET and AI Quality Engineer with 12+ years of experience in automation architecture, AI evaluation, cloud-native quality engineering, and scalable testing platforms.
tracking:AI TestingLLM EvaluationAgentic AIPlaywright AutomationCloud Quality EngineeringScalable Test Infrastructure
quality_signal: production_ready
LLM evals, automation architecture, and cloud quality systems.
12+
Years Experience
80%+
E2E Coverage
45%
Regression Reduction
30%
Faster Execution
20%
Flaky Test Reduction
PlaywrightPythonSeleniumCypressRobot FrameworkLangChainLangGraphPromptFooDeepEvalOpenAI APIAWSCI/CDPerformance TestingAPI TestingAI Agent TestingMCPAgentic AIPlaywrightPythonSeleniumCypressRobot FrameworkLangChainLangGraphPromptFooDeepEvalOpenAI APIAWSCI/CDPerformance TestingAPI TestingAI Agent TestingMCPAgentic AI
Identity
A quality engineer for the AI era.
Srihari operates at the intersection of automation architecture, LLM evaluation, agent validation, and cloud-native release confidence.
The portfolio is designed around one clear signal: Srihari helps teams ship AI-powered systems with measurable trust. That means test architecture, prompt evaluation, hallucination detection, API reliability, performance baselines, and release gates that leaders can understand.
Automation Architect
LLM Testing Specialist
Cloud QA Strategist
Agentic AI Validator
> initializing_ai_quality_engineer.exe
> loading_playwright_framework...
> validating_llm_responses...
> scanning_agentic_tool_calls...
> publishing_quality_signal: PASS
AI Expertise
Evaluation systems for products where correctness matters.
Hallucination & Risk Detection
Builds adversarial test suites, groundedness checks, red-team prompts, and production scorecards for AI reliability.
Agentic AI Validation
Tests planners, memory, MCP tools, retrieval, action execution, fallback flows, and human-in-the-loop controls.
Automation Architecture
Creates scalable Playwright, Cypress, Selenium, API, and performance frameworks with CI-native observability.
Technical Skills
A senior SDET stack with modern AI depth.
PlaywrightPythonSeleniumCypressRobot FrameworkLangChainLangGraphPromptFooDeepEvalOpenAI APIAWSCI/CDPerformance TestingAPI TestingAI Agent TestingMCPAgentic AI
Experience Timeline
Quality leadership across enterprise, education, and product platforms.
Enterprise quality leadership
Wolters Kluwer
Lead SDET / AI Quality Engineer
- Architected automation strategy across product, API, cloud, and AI-assisted workflows.
- Introduced quality gates, observability, and AI evaluation patterns for high-trust releases.
- Led coverage expansion, flake reduction, and regression acceleration programs.
Education technology scale
Chegg
Automation Architect
- Built resilient Playwright, Selenium, API, and CI/CD automation systems.
- Drove scalable test infrastructure for large product surfaces and fast release cycles.
- Improved execution speed and confidence with parallelization and smart test selection.
Sports technology platform
PitchVision
Senior QA Automation Engineer
- Established automated quality foundations across web, API, and device-integrated flows.
- Partnered closely with product and engineering to validate performance-sensitive experiences.
- Created reusable automation patterns for evolving product teams.
Featured AI Projects
Systems that turn quality from a checkpoint into an operating advantage.
Voice AI Quality
Scout Integration AI Voice Agent
Problem: Validate an AI voice agent that handles real-time user intent, tool calls, and ambiguous conversation paths.
Architecture: Voice pipeline with transcription, LLM orchestration, tool routing, conversation memory, telemetry, and eval gates.
OpenAI APILangChainPlaywrightDeepEvalAWS
eval.spec.ts
await evalVoiceAgent({ intent: 'schedule_demo', latencyBudget: 1200, grounded: true })32% faster triage
18% higher intent pass rate
24/7 eval suite
Agentic Search
Solution Scout
Problem: Improve solution discovery across complex product knowledge while reducing hallucinated recommendations.
Architecture: RAG workflows, prompt regression tests, retrieval quality scoring, citation checks, and agent trace review.
LangGraphPromptFooMCPPythonOpenAI API
eval.spec.ts
promptfoo eval --config solution-scout.yaml --grader groundedness41% fewer bad answers
2.3x faster QA review
traceable responses
AI Trust & Safety
Honor Shield
Problem: Catch policy-risk responses, jailbreak attempts, and low-confidence model behavior before release.
Architecture: Safety test matrix, synthetic adversarial prompts, confidence thresholds, audit reports, and CI release blocks.
DeepEvalPythonCI/CDAWSAPI Testing
eval.spec.ts
assert_safety(response, policy='academic_integrity', min_score=0.92)58% expanded risk coverage
zero critical escapes
release-ready evidence
Cloud QA Platform
Uversity
Problem: Scale automation and quality telemetry across web, API, data, and AI-powered learning workflows.
Architecture: Cloud execution grid, contract tests, Playwright suites, API checks, perf baselines, and quality dashboards.
PlaywrightAWSSeleniumRobot FrameworkGrafana
eval.spec.ts
npx playwright test --project=chromium --grep @critical --shard=1/480%+ E2E coverage
30% faster runs
20% fewer flakes
Metrics
Recruiter-readable outcomes, not vague ownership.
80%+
Critical E2E automation coverage
45%
Regression cycle reduction
30%
Execution acceleration
20%
Flaky test reduction
12+
Years in quality engineering
Cloud
AWS-native quality systems
AI Testing Philosophy
Trust is engineered through evidence.
Evals
Agents
CI/CD
Cloud
Code
Trust
AI quality is not a single assertion. It is a living system of scenario design, model behavior scoring, retrieval checks, tool-call validation, safety coverage, latency budgets, trace review, and release governance.
Evaluate model behavior with versioned prompts and deterministic scorecards.
Validate tool use, memory, retrieval, and fallback paths as first-class product flows.
Convert test output into leadership-ready quality signals before release.
Certifications / Awards
Credibility markers for high-trust engineering teams.
AI Quality Engineering Leadership
Advanced Test Automation Architecture
Cloud-Native QA Strategy
Performance & API Quality Engineering
LLM Evaluation and Prompt Testing
Enterprise CI/CD Quality Gates
Open Source Contributions
Production-grade tools and frameworks for quality engineers.
Playwright Web Vitals
A Playwright library for measuring and asserting Web Vitals metrics (LCP, FID, CLS) in automated tests. Essential for performance-driven QA.
PlaywrightWeb VitalsPerformance Testing
Quality & Performance
Playwright Fire Reports
Enhanced HTML reporting for Playwright tests with detailed traces, screenshots, videos, and failure analysis. Makes debugging test failures intuitive.
PlaywrightReportingHTML
Automation Excellence
End-to-End Automation Framework
Production-grade test framework combining Playwright, Page Object Model, CI/CD integration, and scalable test organization. Built for enterprise-scale testing.
PlaywrightFrameworkCI/CDPage Object Model
Architecture & Scale
Blog / Insights
Practical guides on automation, testing, and quality engineering.
5 min read
7 min read
6 min read
8 min read
Contact
Build reliable AI systems with a quality leader who speaks product and engineering.
Available for lead SDET, AI quality engineering, automation architecture, and LLM testing specialist roles.
AI Portfolio Assistant
Local recruiter-facing guide
Ask about Srihari's AI testing work, automation architecture, projects, or leadership signal.