Ignacio Solinas
Director of Quality Engineering | AI-First QA Transformation Leader
Executive leader driving large-scale quality transformation, automation strategy, and engineering-driven QA systems across global organizations.
Built and led global QA organizations of 40+ engineers across the US, LATAM, and India
Scaled automation, metrics, and CI/CD quality gates without increasing headcount
Partnered with C-level leadership to align quality strategy with business outcomes
Executive Summary
I am a seasoned Quality Engineering executive with over two decades of experience leading QA organizations, modernizing testing practices, and embedding quality as a strategic capability across complex software ecosystems.
Throughout my career, I have built and scaled global QA teams, introduced metrics-driven decision making, and led automation initiatives that reduced delivery risk while accelerating release velocity. My background spans both Waterfall and Agile environments, with a strong focus on SaaS platforms, CI/CD pipelines, and end-to-end system reliability.
I specialize in transforming QA from a reactive testing function into a proactive, engineering-driven discipline—partnering closely with product, engineering, and executive leadership to align quality strategy with company objectives.
Executive Impact
Global QA Leadership
Built and developed a global QA organization of more than 40 professionals across multiple countries and time zones.
QA Operating Model
Established a comprehensive QA management system covering processes, deliverables, risk management, and performance metrics.
Metrics-Driven Quality
Introduced executive-level quality metrics and reporting to continuously improve SDLC outcomes and release readiness.
Automation at Scale
Leveraged automation to streamline test execution, data creation, and validation, reducing testing effort and delivery timelines.
Leadership Through Change
Successfully navigated senior leadership changes while keeping teams aligned with evolving business priorities.
Organizational Resilience
Implemented cross-training programs to balance workload and eliminate single points of failure.
Leadership Experience
Newfold Digital
QA Director
- Led a distributed Quality Assurance organization of 40 engineers across the US, Argentina, and India, reporting directly to the CTO.
- Reorganized the QA function to improve efficiency while doubling automation capacity across functional, end-to-end, performance, and load testing.
- Implemented open-source automation tooling (TestNG, Selenium, Protractor, REST Assured, Docker) to reduce licensing costs and modernize the QA stack.
- Introduced executive-level quality metrics to support launch readiness decisions, including defect trends, coverage, and automation effectiveness.
- Managed departmental planning and reporting, including oversight of a $6M QA budget.
Web.com
QA Manager
- Managed a QA team of 23 engineers in Argentina supporting development teams across the US and Canada.
- Oversaw delivery of multiple concurrent projects, including planning, execution tracking, reporting, and resource allocation.
- Led Quality Assurance efforts for a full company rebranding and major product relaunch for Network Solutions.
- Partnered closely with engineering and product teams using Agile (SCRUM) methodologies.
- Supported a broad technology stack including Java, .NET, JavaScript, PHP, Android, iOS, SQL, Oracle, HTML, CSS, and XML.
Web.com
Senior QA Analyst
- Performed black-box and security testing for large-scale web applications.
- Designed and executed test cases and served as system point of contact for QA activities.
- Provided smoke testing support and release validation.
- Trained new team members and acted as SharePoint Service Administrator.
- Conducted requirements and documentation analysis to ensure test coverage.
Verizon Business
QA Semi-Senior Analyst
- Acted as application point of contact and coordinated releases and conference calls.
- Automated test cases using SoapUI and contributed to scope and effort estimation.
- Trained new employees and supported distributed QA activities.
- Provided smoke testing and release verification.
HSBC Bank
QA Junior Analyst
- Conducted white-box testing and reviewed code standards across multiple platforms.
- Provided performance improvement suggestions and technical documentation.
- Reviewed QA methodologies including SDLC and RBPM.
- Tested applications across Mainframe, iSeries, and Windows environments.
Venture Studio / Projects in Parallel
Building products and platforms that apply quality engineering principles to real-world problems
CourtBuddy
Product / MarketplaceTennis matchmaking and community platform designed to simplify finding players, organizing matches, and managing play.
Why it matters:
Applies product thinking, user experience, and platform engineering to a real-world consumer application.
QA Shift Left
Thought Leadership / PlatformAn initiative focused on redefining Quality Assurance as an AI-first, engineering-driven discipline.
Why it matters:
Represents a forward-looking vision for Quality Engineering and Quality as a Management discipline.
Point of View
AI-First Quality Engineering
Quality is no longer a manual verification step or a post-development activity. AI-first quality engineering treats quality as an intelligent system—one that continuously analyzes code changes, assesses risk, selects relevant validations, and learns from both test outcomes and production signals. AI augments human judgment by accelerating analysis and reducing blind spots, while deterministic systems retain final authority.
Shift-Left as Strategic Advantage
Embedding quality decisions earlier in the software lifecycle dramatically reduces downstream risk, cost, and rework. By shifting quality intelligence to pull requests and pre-merge workflows, teams detect issues when context is fresh and changes are small. This enables faster feedback, safer releases, and higher engineering throughput without sacrificing control.
Engineering-Driven QA
Modern QA must operate as an engineering discipline, not a reactive testing function. Engineering-driven QA focuses on code, systems, and automation rather than manual execution. Quality engineers design test infrastructure, define invariants, and build scalable validation systems that integrate directly into CI/CD pipelines and engineering workflows.
Quality as a Product
Quality systems should be designed, measured, and evolved like any other critical product. This includes clear ownership, explicit success metrics, and continuous improvement based on real usage and outcomes. Treating quality as a product ensures it delivers measurable business value—reduced incidents, faster delivery, and increased confidence at scale.
Autonomous Testing at Scale
Autonomous testing combines automation, AI agents, and self-healing infrastructure to scale quality without linear headcount growth. AI assists with test selection, failure classification, and maintenance, while humans focus on defining intent, risk boundaries, and system rules. The result is a resilient, scalable quality system that keeps pace with modern software delivery.
Skills & Stack
Leadership & Strategy
- Global QA leadership
- Quality organization design
- Quality governance and operating models
- Executive-level risk communication and reporting
- Platform-level ownership of quality systems
- Stakeholder management
- Metrics-driven decision making
Quality Engineering
- End-to-end testing
- Test planning and management
- Risk-based test strategies informed by code, CI/CD, and production signals
- Defect management
- SDLC optimization
- CI/CD quality gates
Automation & Tooling
- CI/CD pipelines (Jenkins, GitHub Actions)
- AI-assisted test analysis and generation (LLM-driven agents)
- Autonomous test selection and execution strategies
- Self-healing test frameworks (selectors, timing, flaky test mitigation)
- API automation (REST Assured, contract testing)
- UI automation at scale (Selenium-based frameworks)
- Containerized test execution (Docker)
- AI tooling and workflows (Cursor, agent-based automation, prompt-driven development)
Technical Foundations
- Strong understanding of software architecture and system design
- Code-level test analysis (Java-based test suites, assertions, coverage)
- SQL and data validation for backend systems
- Unix/Linux environments and scripting
- API-first and SaaS platform architectures
- Test observability and production feedback loops
- Metadata-driven test organization and coverage mapping
Photos
Let's Connect
Open to discussing quality engineering leadership, AI-first transformation, and strategic opportunities.