

Results-driven Sr. Test Engineer with 16 years of experience in Software QA and Agile methodologies. Expertise in enhancing product reliability and minimizing production issues through data validation and workflow optimization. Focused on fostering continuous improvement in high-pressure environments.
Software QA
Testing & Methodologies
Test Plans, Cases & Processes
Functional Requirements
Regression & Negative Testing
Mobile Testing
UI & Compatibility Testing
Data Interface & Migration Testing
Performance/Load/Stress Testing
Bug Tracking
Test Strategies & Coverages
Test Exit Strategies
QA & QC Standards
Project Management and Bug
Tracking tool
Application support Level 3
Stakeholder Management
Agile & Scrum
AI Integration
Data Integrity system,
Data analysis,
Data building,
Data Validation Testing
ETL Testing,
Big Data Testing,
BI Report Testing
Data Driven Testing,
Test Data Management,
Data Masking,
Data Reconciliation
Defined and executed product vision, strategy, and roadmap for inhouse AI-based products
Aligning with business goals and market opportunities
Owned the complete product lifecycle from ideation, validation,
MVP development, launch, and post launch optimization
Translated business problems, user needs, and technical possibilities into clear, actionable product
Product Strategy & Ownership Built strong product value propositions,
Identified target users, defined use cases, and established success metrics
Collaborated with leadership to prioritize product initiatives based on impact, feasibility
Ensured products delivered measurable business value with a focus on scalability a
Led end-to-end product development from concept to MVP, focusing on solving core user problems
Conducted product validation through user research, market analysis, and competitor benchmarking
Defined MVP scope, avoiding feature overloading and prioritizing high impact functionalities
Created detailed PRDs, user stories, workflows, wireframes, and release plans
AI Product Development Identified and implemented high value AI use cases including
Roadmap Planning & Prioritization Created and managed product roadmaps using frameworks
RICE, MoSCoW, and impact-effort analysis
Prioritized features based on business impact, user value, technical feasibility
Defined AI-specific requirements such as input/output logic, feedback loops, performance metrics,
Test case design
Business process enhancement
Risk management
Drove continuous improvement by aligning product development with user needs, business objectives
Emerging AI capabilities
Identified and mitigated risks related to model performance, bias, and scalability
Ensured AI products met standards for data privacy, security, reliability, and ethical use
AI Governance, Risk & Quality
Partnered with marketing and sales teams to support product
Positioning, demos, and go-to market strategies
Data, Metrics & Performance Defined
Tracked product KPIs including adoption, engagement, retention, and revenue impact
Monitored AI-specific metrics such as response accuracy, latency, cost efficiency,
Worked closely with engineering to define scope, manage constraints,
Deliver high-quality solution
Ensured AI solutions were practical, explainable, secure, and aligned with real business needs