Principal QA Engineer - AI & Cloud Services at AVEVA

📍 Job Overview

Job Title: Principal QA Engineer - AI & Cloud Services
Company: AVEVA
Location: Bengaluru, Karnataka, India (Hybrid)
Job Type: Regular, Full-Time
Category: Quality Assurance, AI & Cloud Services
Date Posted: 2025-08-09
Experience Level: 10+ years

🚀 Role Summary

AI & Cloud Services Quality Assurance: Ensure the quality, performance, and security of AVEVA's Core AI Services within AVEVA Connect, focusing on distributed, cloud-native services and public APIs.
Cloud-Native Testing Expertise: Validate services deployed on Microsoft Azure, designing and implementing automated test suites for APIs, service components, and AI pipelines.
AI Evaluation & Validation: Automate the evaluation of AI system outputs to ensure accuracy, consistency, and safety of responses, defining performance metrics for AI services.
Collaborative & Mentoring Role: Work closely with developers and data scientists to establish service-level quality metrics and observability hooks, mentoring junior testers and fostering a culture of continuous learning and innovation.

💻 Primary Responsibilities

Cloud-Native Service Testing: Perform functional, performance, and security testing on cloud-native services deployed on Microsoft Azure.
Automated Testing Framework Design: Design and implement automated test suites for APIs, service components, and AI pipelines.
AI Output Evaluation: Automate the evaluation of AI system outputs to ensure accuracy, consistency, and safety of responses.
Service-Level Quality Metrics: Collaborate with developers and data scientists to establish service-level quality metrics and observability hooks.
AI Regulatory Compliance: Validate services against AI regulatory frameworks, ensuring traceability, fairness, and robustness in outcomes.
Threat Modeling & Security Validation: Participate in threat modeling and security validation of exposed APIs and AI services.
Early Feedback & Defect Reduction: Provide feedback early in the lifecycle to reduce defects and improve design.
Mentoring & Knowledge Sharing: Mentor junior testers, encourage continuous learning, and contribute to a culture of innovation.

🎓 Skills & Qualifications

Education: Bachelor's degree in Computer Science, Engineering, or a related field. Relevant certifications in software testing or quality assurance are a plus.

Experience: 8+ years of experience in software testing or QA for cloud-native applications, including 2+ years working on AI/ML systems or services.

Required Skills:

Proficient in designing automated testing frameworks
Hands-on experience with Azure DevOps, CI/CD pipelines, and containerized test environments
Strong understanding of API testing, performance profiling, and security testing (including OWASP top 10)
Excellent problem-solving skills, with the ability to analyze complex technical challenges and propose scalable solutions
Experience working in Agile teams and collaborating across global R&D locations
Demonstrated ability to mentor junior team members, fostering a culture of continuous learning and innovation

Preferred Skills:

Familiarity with LLM evaluation techniques, output scoring, and validation frameworks
Understanding of key concepts such as prompt engineering, RAG, model orchestration, and hallucination detection
Experience in testing for accuracy, relevance, and consistency of AI model predictions/generations
Awareness of AI safety, bias detection, and explainability techniques
Experience ensuring compliance with AI regulations and standards (e.g., NIST AI RMF, EU AI Act)
Strong belief in ethical AI practices, transparency, and end-user trust

📊 Web Portfolio & Project Requirements

Portfolio Essentials:

Cloud-Native Service Testing Cases: Demonstrate functional, performance, and security testing cases for cloud-native services deployed on Microsoft Azure.
Automated Test Suite Examples: Showcase automated test suites designed and implemented for APIs, service components, and AI pipelines.
AI Output Evaluation Examples: Present automated evaluation of AI system outputs, ensuring accuracy, consistency, and safety of responses.
Service-Level Quality Metrics Documentation: Display examples of service-level quality metrics and observability hooks established in collaboration with developers and data scientists.

Technical Documentation:

Test Case Documentation: Provide detailed documentation of test cases, including test data, expected results, and any relevant test environment setup instructions.
Automation Scripts & Frameworks: Share code samples or scripts demonstrating your approach to automated testing framework design and implementation.
AI Evaluation & Validation Tools: Showcase any custom tools or frameworks developed for AI output evaluation and validation.
Test Results & Reports: Present test results and reports, highlighting any performance bottlenecks, security vulnerabilities, or other issues identified during testing.

💵 Compensation & Benefits

Salary Range: INR 25,00,000 - 35,00,000 per annum (Based on experience and skills)

Benefits:

Gratuity
Medical and accidental insurance
Very attractive leave entitlement
Emergency leave days
Childcare support
Maternity, paternity, and adoption leaves
Education assistance program
Home office set up support (for hybrid roles)
Well-being support

Working Hours: 40 hours per week, with flexible hours and remote work options available.

🎯 Team & Company Context

🏢 Company Culture

Industry: AVEVA operates in the industrial software industry, focusing on AI and cloud services for enterprise-level clients.

Company Size: AVEVA has over 6,500 employees worldwide, with a global presence in over 40 countries.

Founded: AVEVA was founded in 1967 and has since grown into a global leader in industrial software, with a strong focus on research and development.

Team Structure:

The AI & Cloud Services team is part of AVEVA's global R&D organization, consisting of over 2,000 developers working on various industrial automation and engineering products.
The team follows Agile methodologies, working in cross-functional Scrum teams to deliver high-quality products and services.
The Principal QA Engineer role reports directly to the R&D Senior Manager - AI Core Services Development.

Development Methodology:

AVEVA follows Agile development methodologies, with a focus on continuous integration, continuous delivery, and continuous improvement.
The company uses Azure DevOps for version control, CI/CD pipelines, and project management, with a strong emphasis on automated testing and quality assurance.
AVEVA encourages a culture of innovation, learning, and collaboration, with regular hackathons, tech talks, and training opportunities.

Company Website: https://www.aveva.com/

📝 Enhancement Note: AVEVA's global presence and focus on AI and cloud services provide ample opportunities for professional growth and exposure to cutting-edge technologies.

📈 Career & Growth Analysis

AI & Cloud Services Quality Assurance Career Level: The Principal QA Engineer role is a senior-level position, responsible for defining and implementing quality assurance strategies for AVEVA's Core AI Services. This role requires a deep understanding of cloud-native services, AI systems, and quality assurance best practices.

Reporting Structure: The Principal QA Engineer reports directly to the R&D Senior Manager - AI Core Services Development and works closely with developers, data scientists, and other quality assurance professionals within the team.

Technical Impact: This role has a significant impact on the quality, performance, and security of AVEVA's Core AI Services, ensuring that enterprise clients receive reliable, accurate, and safe AI-driven solutions.

Growth Opportunities:

Technical Leadership: With experience and demonstrated success, the Principal QA Engineer may progress to a leadership role, managing a team of quality assurance professionals and driving the quality assurance strategy for AVEVA's AI & Cloud Services.
Architecture & Design: As the Principal QA Engineer gains expertise in AI systems and cloud-native services, they may transition into an architecture or design role, focusing on the design and implementation of AI services and quality assurance frameworks.
Product Management: With a strong understanding of AI systems, cloud-native services, and quality assurance best practices, the Principal QA Engineer may move into a product management role, responsible for defining product roadmaps, features, and requirements for AVEVA's AI & Cloud Services.

📝 Enhancement Note: AVEVA's focus on AI and cloud services, along with its global presence and commitment to innovation, provides ample opportunities for technical growth and career progression within the quality assurance domain.

🌐 Work Environment

Office Type: AVEVA's Bengaluru office is a modern, collaborative workspace designed to foster innovation and creativity. The office features open-plan workspaces, dedicated meeting rooms, and breakout areas for informal discussions and team building.

Office Location(s): AVEVA's Bengaluru office is located in the heart of the city's tech hub, with easy access to public transportation and amenities. The office is also close to several residential neighborhoods, making it an attractive option for both local and expatriate employees.

Workspace Context:

Collaborative Work Environment: AVEVA's Bengaluru office encourages collaboration and knowledge sharing, with open-plan workspaces and dedicated team areas for cross-functional projects and initiatives.
State-of-the-Art Technology: The office is equipped with the latest hardware, software, and development tools, ensuring that employees have access to the resources they need to deliver high-quality products and services.
Flexible Work Arrangement: AVEVA offers a hybrid work arrangement, with employees expected to be in the office three days a week. This flexible approach allows employees to balance their work and personal lives while maintaining a strong connection to the team and company culture.

Work Schedule: AVEVA's standard work schedule is Monday to Friday, 9:00 AM to 6:00 PM, with a one-hour lunch break. The company offers flexible working hours and remote work options to accommodate employees' personal schedules and responsibilities.

📝 Enhancement Note: AVEVA's modern, collaborative work environment and flexible work arrangement provide an ideal setting for quality assurance professionals to thrive and grow their careers.

📄 Application & Technical Interview Process

Interview Process:

Phone Screen: A brief phone or video call to assess communication skills, cultural fit, and basic technical knowledge.
Technical Assessment: A hands-on technical assessment, focusing on cloud-native service testing, automated testing framework design, and AI output evaluation. The assessment may include a take-home task or live coding exercise.
On-site Interview: A face-to-face interview with the hiring manager, team members, and other stakeholders. This interview focuses on problem-solving skills, technical depth, and cultural fit.
Final Decision: A final decision is made based on the candidate's performance throughout the interview process, with a focus on technical skills, cultural fit, and alignment with AVEVA's values and mission.

Portfolio Review Tips:

Cloud-Native Service Testing Cases: Highlight functional, performance, and security testing cases for cloud-native services deployed on Microsoft Azure, demonstrating your ability to validate complex, distributed systems.
Automated Test Suite Examples: Showcase automated test suites designed and implemented for APIs, service components, and AI pipelines, emphasizing your proficiency in automated testing framework design and implementation.
AI Output Evaluation Examples: Present automated evaluation of AI system outputs, ensuring accuracy, consistency, and safety of responses. Highlight any custom tools or frameworks developed for AI output evaluation and validation.
Service-Level Quality Metrics Documentation: Display examples of service-level quality metrics and observability hooks established in collaboration with developers and data scientists, demonstrating your ability to work effectively with cross-functional teams.

Technical Challenge Preparation:

Cloud-Native Service Testing: Brush up on your knowledge of cloud-native services, API testing, and performance profiling. Familiarize yourself with Microsoft Azure and any relevant Azure services used by AVEVA.
Automated Testing Framework Design: Review your understanding of automated testing frameworks, CI/CD pipelines, and containerized test environments. Ensure you are comfortable with Azure DevOps and other relevant testing tools.
AI Output Evaluation: Study AI output evaluation techniques, AI system outputs, and AI-specific quality assurance best practices. Familiarize yourself with AI regulations and standards, such as NIST AI RMF and EU AI Act.

ATS Keywords: (Organized by category)

Programming Languages: Python, Java, C#, JavaScript, TypeScript
Web Frameworks: React, Angular, Vue.js, ASP.NET Core
Server Technologies: Microsoft Azure, AWS, Google Cloud Platform
Databases: SQL Server, Azure Cosmos DB, Azure SQL Database, MongoDB
Tools: Azure DevOps, JIRA, Confluence, Postman, Swagger, Newman, OWASP ZAP, Burp Suite
Methodologies: Agile, Scrum, Kanban, Test-Driven Development, Behavior-Driven Development
Soft Skills: Communication, Teamwork, Problem-Solving, Adaptability, Innovation, Mentoring
Industry Terms: AI, Machine Learning, Deep Learning, Natural Language Processing, Computer Vision, Cloud-Native, Microservices, APIs, CI/CD, DevOps, Quality Assurance, Software Testing

📝 Enhancement Note: AVEVA's focus on AI and cloud services, along with its commitment to innovation and continuous learning, requires quality assurance professionals to stay up-to-date with the latest technologies, best practices, and industry trends.

🛠 Technology Stack & Web Infrastructure

Frontend Technologies:

React: A JavaScript library for building user interfaces, widely used for web and mobile applications.
Redux: A predictable state container for JavaScript apps, used for managing application state and data flow.
Material-UI: A popular React UI library that implements Google's Material Design system, providing a consistent and visually appealing user experience.

Backend & Server Technologies:

ASP.NET Core: A cross-platform, high-performance, open-source framework for building modern, cloud-based, Internet-connected applications.
Microsoft Azure: A comprehensive cloud computing platform provided by Microsoft, offering a wide range of services, including compute, storage, databases, and AI/ML tools.
Azure Functions: A serverless compute platform that enables customers to run event-driven code in response to triggers such as HTTP requests, database changes, or timer schedules.

Development & DevOps Tools:

Azure DevOps: A comprehensive set of development, project management, and deployment tools provided by Microsoft, enabling customers to plan, track, and release their software projects.
Git: A distributed version control system that enables multiple developers to work together on non-linear development projects.
Docker: A platform for developing, deploying, and running applications using containers, providing a consistent and isolated environment for applications to run.
Kubernetes: An open-source platform for automating deployment, scaling, and management of containerized applications, providing a portable, extensible, and scalable infrastructure for deploying and managing applications.

📝 Enhancement Note: AVEVA's technology stack, which includes popular and cutting-edge tools, provides quality assurance professionals with ample opportunities to gain expertise in modern web development, cloud, and AI technologies.

👥 Team Culture & Values

AI & Cloud Services Quality Assurance Values:

Quality-Focused: A commitment to delivering high-quality products and services, with a strong emphasis on testing, validation, and quality assurance best practices.
Innovation-Driven: A culture that encourages continuous learning, experimentation, and the adoption of new technologies and methodologies.
Collaborative: A team-oriented approach that values open communication, knowledge sharing, and cross-functional collaboration.
Customer-Centric: A focus on understanding and addressing the needs of AVEVA's enterprise clients, ensuring that AI & Cloud Services meet their unique requirements and expectations.
Ethical: A commitment to ethical AI practices, transparency, and end-user trust, with a strong emphasis on fairness, accountability, and responsible AI development.

Collaboration Style:

Cross-Functional Integration: AVEVA's AI & Cloud Services team works closely with developers, data scientists, designers, and other stakeholders to ensure that AI & Cloud Services meet the needs of enterprise clients and align with AVEVA's broader product strategy.
Code Review Culture: AVEVA encourages a culture of code review and peer programming, with a strong emphasis on knowledge sharing, collaboration, and continuous learning.
Knowledge Sharing: AVEVA fosters a culture of knowledge sharing, with regular tech talks, training opportunities, and hackathons that encourage employees to learn from one another and collaborate on innovative projects.

📝 Enhancement Note: AVEVA's AI & Cloud Services team values provide a strong foundation for quality assurance professionals to grow their careers, with a focus on quality, innovation, collaboration, and customer-centricity.

⚡ Challenges & Growth Opportunities

Technical Challenges:

Cloud-Native Service Testing: Validate distributed, cloud-native services and public APIs that form the foundation for enterprise AI capabilities, ensuring they are robust, secure, and scalable.
AI Output Evaluation: Automate the evaluation of AI system outputs to ensure accuracy, consistency, and safety of responses, with a focus on AI-specific evaluation tools and frameworks.
AI Regulatory Compliance: Validate services against AI regulatory frameworks, ensuring traceability, fairness, and robustness in outcomes, with a strong emphasis on ethical AI practices and user trust.
Threat Modeling & Security Validation: Participate in threat modeling and security validation of exposed APIs and AI services, with a focus on OWASP top 10 vulnerabilities and other common security threats.

Learning & Development Opportunities:

Technical Skill Development: AVEVA offers numerous training opportunities, including online courses, workshops, and hackathons, enabling quality assurance professionals to develop their technical skills and stay up-to-date with the latest industry trends.
Conference Attendance & Certification: AVEVA encourages employees to attend industry conferences and obtain relevant certifications, providing them with the opportunity to learn from experts, network with peers, and enhance their professional development.
Technical Mentorship & Leadership: AVEVA provides mentorship opportunities, with experienced team members offering guidance and support to junior quality assurance professionals. These mentorship relationships can lead to technical leadership roles and architecture decision-making opportunities.

📝 Enhancement Note: AVEVA's commitment to innovation, continuous learning, and professional development provides quality assurance professionals with ample opportunities to overcome technical challenges and grow their careers within the AI & Cloud Services domain.

💡 Interview Preparation

Technical Questions:

Cloud-Native Service Testing: Describe your experience with cloud-native service testing, API testing, and performance profiling. Provide examples of complex cloud-native services you have tested and any unique challenges you faced.
Automated Testing Framework Design: Explain your approach to automated testing framework design and implementation. Describe any custom frameworks you have developed and the tools you used to create them.
AI Output Evaluation: Discuss your experience with AI output evaluation, AI system outputs, and AI-specific quality assurance best practices. Provide examples of AI output evaluation techniques you have implemented and any challenges you faced.

Company & Culture Questions:

AI & Cloud Services Quality Assurance Culture: Explain how you would contribute to AVEVA's AI & Cloud Services quality assurance culture, with a focus on quality, innovation, collaboration, and customer-centricity.
AI & Cloud Services Methodologies: Describe your understanding of AI & Cloud Services methodologies, including Agile development, CI/CD pipelines, and containerized test environments. Explain how you would apply these methodologies to ensure the quality, performance, and security of AVEVA's Core AI Services.
AI & Cloud Services Team Dynamics: Discuss your experience working in cross-functional teams and collaborating with developers, data scientists, and other stakeholders. Explain how you would work effectively with AVEVA's AI & Cloud Services team to ensure the success of AI & Cloud Services projects.

Portfolio Presentation Strategy:

Cloud-Native Service Testing Cases: Present functional, performance, and security testing cases for cloud-native services deployed on Microsoft Azure, highlighting any unique challenges or complexities you faced during testing.
Automated Test Suite Examples: Showcase automated test suites designed and implemented for APIs, service components, and AI pipelines, emphasizing your ability to create efficient, maintainable, and scalable testing solutions.
AI Output Evaluation Examples: Present automated evaluation of AI system outputs, ensuring accuracy, consistency, and safety of responses. Highlight any custom tools or frameworks you developed for AI output evaluation and validation, and explain how they contributed to the overall quality of AI & Cloud Services.
Service-Level Quality Metrics Documentation: Display examples of service-level quality metrics and observability hooks established in collaboration with developers and data scientists, demonstrating your ability to work effectively with cross-functional teams and drive the quality assurance strategy for AI & Cloud Services.

📝 Enhancement Note: AVEVA's commitment to innovation, continuous learning, and professional development requires quality assurance professionals to be well-prepared and able to articulate their technical expertise, problem-solving skills, and cultural fit during the interview process.

📌 Application Steps

To apply for the Principal QA Engineer - AI & Cloud Services position at AVEVA:

Submit Your Application: Visit the AVEVA careers portal (https://aveva.wd3.myworkdayjobs.com/AVEVA_careers/job/Bangalore-India/Principal-QA-Engineer---AI---Cloud-Services_R011754) and submit your cover letter and resume, highlighting your relevant experience, skills, and qualifications.
Prepare Your Portfolio: Tailor your portfolio to showcase your cloud-native service testing cases, automated test suite examples, AI output evaluation examples, and service-level quality metrics documentation. Ensure your portfolio demonstrates your ability to create efficient, maintainable, and scalable testing solutions, with a focus on AI & Cloud Services quality assurance best practices.
Research AVEVA: Familiarize yourself with AVEVA's AI & Cloud Services, company culture, and values. Understand the unique challenges and opportunities presented by AI & Cloud Services, and how your skills and experience align with AVEVA's quality assurance needs.
Prepare for Technical Challenges: Brush up on your knowledge of cloud-native services, API testing, performance profiling, AI output evaluation, and AI-specific quality assurance best practices. Ensure you are comfortable with Microsoft Azure, Azure DevOps, and other relevant testing tools.
Practice Interview Questions: Review the technical and company & culture questions outlined in the 'Interview Preparation' section, and practice your responses to ensure you can articulate your technical expertise, problem-solving skills, and cultural fit effectively during the interview process.

Good luck with your application! With your comprehensive understanding of AI & Cloud Services quality assurance, cloud-native services, and AI-specific quality assurance best practices, you are well-prepared to succeed in the Principal QA Engineer - AI & Cloud Services role at AVEVA.

Principal QA Engineer - AI & Cloud Services