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Sample resume with fictional details. Replace the content with your own experience and qualifications. Format: Executive.

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Sanjay Kulkarni

VP of Quality Engineering · 19 years experience

Bangalore, Karnataka

[Your email] · [Your phone]
[Your LinkedIn URL] · [Your GitHub / Portfolio URL]

Professional Summary

  • Nineteen-year veteran of software quality engineering leading a 220-person global Quality Engineering division distributed across Bangalore, London, and Austin reporting to the Chief Technology Officer
  • Executive sponsor of the company's $15M AI testing transformation initiative delivering AI-native quality infrastructure, GitHub Copilot Enterprise, and LLM evaluation platforms across all product lines
  • Drives board-level AI quality ROI reporting demonstrating $8M annual cost avoidance through automated AI testing, predictive defect detection, and quality acceleration programs
  • Oversaw the company's EU AI Act compliance quality program covering 28 high-risk AI features serving EU-regulated financial services and healthcare customers across 14 countries
  • Transformed QA from a cost center into a Quality Engineering Platform business unit with 12 internal customers generating $3.2M annual chargeback revenue through shared testing services
  • Reduced time-to-production by 40% over 2 years through AI-accelerated testing automation, intelligent test selection, self-healing frameworks, and quality gate optimization
  • Founded the company's AI Safety Testing function — the first dedicated AI safety QA team in the Indian SaaS sector — aligned with the NIST AI Risk Management Framework
  • Speaker at Davos SideBusiness Forum, CIO India Summit 2024, and AWS re:Inforce on enterprise AI quality transformation, responsible AI governance, and quality ROI at scale

Work Experience

VP of Quality Engineering

[Company name] · [Start date – End date]

19 years in quality engineering progressing from QA Engineer through QA Manager, Director, to VP Quality Engineering. Currently leads a 220-person Quality Engineering division with a $5.5M annual budget reporting to the CTO. Oversees quality for products serving 80+ enterprise clients across $420M ARR. Provides board-level reporting on AI quality risk, regulatory compliance status, and engineering investment ROI. Manages 4 Director-level direct reports and 12 senior QA managers globally.

Responsibilities

  • Lead 220-person global Quality Engineering division with 4 VP-direct reports and 12 senior managers across 3 geographies
  • Own quality engineering P&L of $5.5M annually including headcount planning, tool investment, and organizational design
  • Provide board-level AI quality risk reporting, regulatory compliance status, and engineering ROI metrics quarterly
  • Serve as executive sponsor for AI testing transformation, AI safety testing, and quality culture programs
  • Drive QE platform strategy: shared testing infrastructure, AI evaluation APIs, and quality-as-a-service for product squads
  • Partner with CISO, Chief Legal Officer, and Chief Product Officer on AI governance, privacy, and product quality strategy
  • Represent company at NASSCOM, G20 Digital Economy forums, and international AI quality standards bodies
  • Set 3 to 5 year quality engineering technology roadmap aligned to company AI-first product strategy and board vision
  • Manage senior leadership talent pipeline: VP hiring, Director succession planning, and principal engineer retention
  • Negotiate executive-level vendor partnerships with GitHub, AWS, and enterprise AI testing solution providers
  • Drive engineering culture of quality, psychological safety, continuous learning, and innovation through quarterly programs
  • Present QE performance reports, AI quality metrics, and strategic investment proposals to the board of directors

Project Experience

$15M AI Testing Transformation

Executive sponsor and program director for the 24-month AI testing transformation delivering GitHub Copilot Enterprise for test generation, self-healing automation platform, DeepEval LLM evaluation pipeline, and the company-wide AI Safety Testing function. Technologies: GitHub Copilot Enterprise, DeepEval, Playwright, OpenTelemetry, AI Safety Frameworks, Program Governance.

EU AI Act Compliance Program

Led quality coverage design for 28 high-risk EU AI Act features spanning transparency obligation testing, algorithmic bias detection, data quality validation, human oversight verification, and technical documentation compliance. Technologies: Compliance Frameworks, DeepEval, RAGAS, Explainability Testing, Fairlearn, Risk Classification.

Quality Engineering Platform Business Unit

Transformed QA into an internal platform business with SLA-backed testing APIs, shared automation infrastructure, and AI evaluation-as-a-service serving 12 product squads with transparent chargeback billing generating $3.2M annual revenue. Technologies: API Gateway, GitHub Actions, DeepEval, Playwright Platform, AWS, Billing and Chargeback Automation.

AI Safety Testing Function

Founded and staffed the company's AI Safety Testing function responsible for adversarial AI testing, algorithmic fairness evaluation, NIST AI RMF control verification, and AI incident response preparation for all production AI deployments. Technologies: Garak, PromptBench, OWASP LLM Top 10, NIST AI RMF, Python, Fairlearn, Responsible AI Toolbox.