Verification Engineer

Sunovaa Tech; Bengaluru; Hybrid; INR 900,000 - 1,500,000; Full time

Job summary

Sunovaa Tech in Bengaluru, India, is seeking a Verification Engineer with 3–5 years of QA/testing experience to shape testing strategies for data pipelines and AI-driven solutions. Hybrid work arrangement.

You will validate data integrity, evaluate AI model outputs, and collaborate with data engineers, scientists, and product managers to ensure business requirements are met. Strong SQL and ETL testing skills are preferred.

Qualifications

• 3–5 years of QA/testing experience, preferably with data products, data pipelines, or analytics platforms
• Advanced SQL skills for data validation, query analysis, and complex data verification
• Proven experience testing data warehouses, ETL processes, or analytics solutions
• Ability to evaluate and test AI/ML model outputs, including accuracy metrics and result validation
• Hands-on experience with Azure DevOps for test management, work item tracking, and CI/CD pipeline validation
• Strong knowledge of testing best practices (unit, integration, system, regression testing)
• Verifying complete end-to-end AI pipelines from data ingestion to model inference and result delivery

Responsibilities

• Test Strategy & Planning: Develop comprehensive testing plans for data products and AI/ML solutions, including unit, integration, and end-to-end testing
• Data Validation: Verify data accuracy, completeness, and consistency across pipelines using SQL queries and validation frameworks
• AI Model Testing: Evaluate AI/ML model outputs for accuracy, bias, performance, and reliability
• Test Automation: Design and implement automated test suites for data pipelines and AI predictions using appropriate tools and frameworks
• Azure DevOps Management: Utilize Azure DevOps for test case management, bug tracking, CI/CD pipeline validation, and test execution tracking
• Performance Testing: Conduct performance and load testing on data pipelines and AI inference systems
• Documentation: Create detailed test plans, test cases, and quality reports; maintain comprehensive testing documentation
• Collaboration: Work closely with data engineers, data scientists, and product managers to understand requirements and acceptance criteria
• Continuous Improvement: Identify testing gaps, recommend improvements, and stay updated with emerging testing methodologies for AI/data products
• Production Support: Monitor and troubleshoot issues in production data products and AI systems

Skills

QA testing

SQL

Data product testing

AI/ML evaluation

Testing methodologies

AI pipeline testing

Tools

Azure DevOps

Snowflake

Git

Job description

Verification Engineer - Job Description

Position Title: Verification Engineer (Data Products & AI)

Experience Level: 3-5 years of professional experience in quality assurance, testing, or verification engineering

Modes of Work: Hybrid

About the Role

We are seeking a Verification Engineer to ensure the quality, reliability, and performance of our data products and AI-driven solutions. You will design and execute comprehensive testing strategies, validate data pipelines, and verify AI model outputs to ensure they meet business requirements and maintain data integrity.

Key Responsibilities

• Test Strategy & Planning: Develop comprehensive testing plans for data products and AI/ML solutions, including unit, integration, and end-to-end testing
• Data Validation: Verify data accuracy, completeness, and consistency across pipelines using SQL queries and validation frameworks
• AI Model Testing: Evaluate AI/ ML model outputs for accuracy, bias, performance, and reliability
• Test Automation: Design and implement automated test suites for data pipelines and AI predictions using appropriate tools and frameworks
• Azure DevOps Management: Utilize Azure DevOps for test case management, bug tracking, CI/CD pipeline validation, and test execution tracking
• Performance Testing: Conduct performance and load testing on data pipelines and AI inference systems
• Documentation: Create detailed test plans, test cases, and quality reports; maintain comprehensive testing documentation
• Collaboration: Work closely with data engineers, data scientists, and product managers to understand requirements and acceptance criteria
• Continuous Improvement: Identify testing gaps, recommend improvements, and stay updated with emerging testing methodologies for AI/data products
• Production Support: Monitor and troubleshoot issues in production data products and AI systems

Required Qualifications

• 3-5 years of experience in QA/testing, preferably with data products, data pipelines, or analytics platforms
• SQL Expertise: Advanced SQL skills for data validation, query analysis, and complex data verification
• Data Product Testing: Proven experience testing data warehouses, ETL processes, or analytics solutions
• AI/ML Evaluation: Ability to evaluate and test AI/ML model outputs, including accuracy metrics and result validation
• Azure DevOps: Hands-on experience with Azure DevOps for test management, work item tracking, and CI/CD pipeline validation. Testing ETL/ELT processes, data transformations, and data quality rules
• Testing Methodologies: Strong knowledge of testing best practices (unit, integration, system, regression testing)
• End-to-End AI Pipeline Testing: Verifying complete workflows from data ingestion to model inference and result delivery

Preferred Qualifications (Good to Have)

• Python/Programming: Basic programming knowledge in Python, Java, or similar languages for test automation
• Data Quality Tools: Familiarity with data quality platforms
• Snowflake/Cloud Databases: Experience testing on Snowflake, Azure SQL, or similar cloud data platforms
• AI/ML Concepts: Understanding of machine learning fundamentals, model evaluation metrics, and bias detection
• CI/CD Pipelines: Understanding of continuous integration and continuous deployment practices
• Git & Version Control: Familiarity with Git for test code management
• Agile/Scrum: Experience working in Agile environments

Soft Skills

• Strong communication and documentation abilities
• Ability to collaborate effectively with cross-functional teams (engineers, scientists, product managers)
• Detail-oriented with a mindset for quality excellence
• Proactive problem-solving and critical thinking
• Adaptability and willingness to learn new technologies
• Ownership mentality and commitment to product quality
• Time management and ability to manage multiple testing initiatives Role & responsibilities