Software Development Engineer in Test (API, UI & Agentic Systems)

📍 Job Overview

Job Title: Software Development Engineer in Test (API, UI & Agentic Systems)

Company: NetApp, Inc.

Location: Bangalore, Karnataka, India

Job Type: Full-Time

Category: Quality Engineering / Software Development

Date Posted: 2026-08-20

Experience Level: Mid-Level (4+ years)

Remote Status: On-site

🚀 Role Summary

• Design and develop advanced, scalable automation frameworks for validating modern distributed applications, APIs, web experiences, and cutting-edge AI-powered agentic workflows.

• Collaborate closely with engineering, product management, and quality assurance teams to define and execute comprehensive automation strategies.

• Enhance release confidence and accelerate product delivery cycles through the implementation of innovative and efficient testing solutions.

• Work at the forefront of quality engineering, bridging traditional automation practices with emerging AI and machine learning technologies.

• Drive the adoption of intelligent testing techniques, including AI-assisted test generation and autonomous testing solutions.

📝 Enhancement Note: This role is positioned within a rapidly evolving technological landscape, emphasizing the integration of traditional software testing with advanced AI capabilities. The focus on "agentic systems" and "AI-powered workflows" indicates a need for candidates who can think beyond conventional automation and embrace novel approaches to quality assurance in AI-driven products. The "Software Engineer 3" title suggests a mid-level position requiring significant hands-on experience and the ability to influence technical direction.

📈 Primary Responsibilities

• Design, develop, and maintain robust, reusable, and scalable automation frameworks for REST and GraphQL APIs, modern web applications, user interfaces, and complex AI-powered applications and agentic workflows.

• Build and manage scalable test infrastructure that comprehensively supports functional, integration, regression, performance, and end-to-end testing scenarios.

• Create sophisticated automated validation mechanisms specifically for Large Language Model (LLM)-based systems, AI agents, multi-agent workflows, and Retrieval-Augmented Generation (RAG) applications.

• Seamlessly integrate automation solutions into CI/CD pipelines to enable continuous quality validation, reduce cycle times, and facilitate rapid iteration.

• Partner proactively with development and architecture teams to establish effective shift-left testing practices and enhance the inherent testability of software components.

• Define, track, and report on key quality metrics, test coverage goals, and automation standards across multiple engineering teams to ensure consistent quality benchmarks.

• Investigate production issues and customer-reported defects, identify root causes, pinpoint automation gaps, and implement improvements to enhance overall test effectiveness and reliability.

• Contribute significantly to the adoption and implementation of AI-assisted testing techniques, intelligent test generation, and autonomous testing solutions to further optimize the quality engineering process.

📝 Enhancement Note: The responsibilities highlight a deep dive into advanced testing areas, particularly for AI and distributed systems. The emphasis on "agentic workflows," "LLM-based systems," and "RAG applications" suggests that candidates will be expected to design and implement testing strategies for complex, emergent AI functionalities. The integration into CI/CD and collaboration for shift-left testing are standard but critical for a mid-level engineer driving quality initiatives.

🎓 Skills & Qualifications

Education:

• Bachelor's or Master's degree in Computer Science, Engineering, or a closely related technical discipline.

• Typically requires a minimum of 4+ years of relevant professional experience for a Software Engineer 3 level. Experience:

• Minimum of 4 years of hands-on experience in software development or comprehensive test automation.

• Proven track record of successfully building automation frameworks from inception to deployment.

• Experience with modern software development lifecycles (SDLC) and various testing methodologies (e.g., Agile, Scrum). Required Skills:

• Strong programming proficiency in one or more languages such as Python, JavaScript, or TypeScript.

• Extensive experience testing REST APIs using frameworks and tools like Pytest, Playwright API Testing, or similar.

• Proven ability to automate web applications and user interfaces using tools such as Playwright, Cypress, or Selenium.

• Solid understanding of version control systems, specifically Git, for code management and collaboration.

• Experience with Continuous Integration/Continuous Deployment (CI/CD) pipelines and their integration with automated testing.

• Familiarity with microservices architectures and the unique testing challenges of distributed systems.

• Hands-on experience with cloud-native application testing principles and practices. Preferred Skills:

• Prior experience testing AI/GenAI applications, including LLM-based systems and agentic workflows.

• Familiarity with key AI/LLM technologies and frameworks:

  • OpenAI, Azure OpenAI, Anthropic, or other major LLM platforms.
  • LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel, or similar orchestration libraries.
  • MCP-based integrations for AI systems.
  • Vector databases (e.g., Pinecone, Weaviate, Milvus) and Retrieval-Augmented Generation (RAG) architectures.

• Experience building specialized evaluation frameworks for AI systems, focusing on:

  • Agent correctness and decision-making logic.
  • Hallucination detection and mitigation strategies.
  • Workflow reliability and robustness.
  • Prompt engineering and model regression testing.

• Practical experience with containerization technologies like Docker and orchestration platforms like Kubernetes.

• Experience with major cloud platforms such as AWS, Azure, or GCP for deploying and testing applications.

• Knowledge of performance testing, security testing, and resilience testing methodologies.

• Exposure to observability tools and telemetry-driven quality engineering practices.

📝 Enhancement Note: The required skills are robust, focusing on core programming languages and established automation tools. The preferred qualifications are highly specialized, indicating NetApp's investment in AI and agentic systems. Candidates with experience in these niche areas (LLMs, RAG, specific AI frameworks) will have a significant advantage. The "What Makes You Successful" section from the original description should be integrated here to provide a holistic view of candidate attributes.

📊 Process & Systems Portfolio Requirements

Portfolio Essentials:

• Demonstrated ability to design and implement end-to-end automation frameworks that cover API, UI, and complex system integrations.

• Case studies showcasing the development and optimization of testing processes for distributed systems or microservices architectures.

• Examples of implemented CI/CD integrations for automated testing, highlighting efficiency gains and reduced deployment friction.

• Projects that illustrate experience with cloud-native testing environments and containerized applications (Docker, Kubernetes).

• Evidence of contributions to improving testability and establishing robust quality gates within development lifecycles. Process Documentation:

• Documented workflows for developing new automation scripts and maintaining existing test suites.

• Examples of process documentation for integrating automated tests into CI/CD pipelines, including trigger mechanisms and reporting.

• Records of process improvements implemented to enhance test execution speed, stability, and coverage.

• Methodologies for creating and maintaining test data management strategies for complex systems.

• Processes for investigating production incidents, performing root cause analysis, and developing automated checks to prevent recurrence.

📝 Enhancement Note: For a role of this nature, a portfolio demonstrating practical application of automation skills is crucial. Emphasis should be placed on projects that show not just the ability to write code, but also to architect, implement, and maintain robust testing solutions that contribute to business objectives like faster release cycles and higher product quality. For AI-specific testing, showcasing experience with evaluation metrics and frameworks would be highly beneficial.

💵 Compensation & Benefits

Salary Range:

Based on industry benchmarks for a Software Development Engineer in Test with 4+ years of experience in Bangalore, India, the estimated annual salary range is ₹12,00,000 to ₹25,00,000. This range accounts for the specialized nature of testing AI/agentic systems and the demand for skilled engineers in the Bangalore tech market. The exact compensation will depend on the candidate's specific experience, skill set, and performance during the interview process.

Benefits:

• Comprehensive health insurance coverage (medical, dental, vision).

• Retirement savings plans (e.g., Provident Fund contributions).

• Paid time off, including vacation days, sick leave, and holidays.

• Opportunities for professional development, including training, certifications, and conference attendance.

• Access to NetApp's employee assistance program for personal and professional support.

• Potential for stock options or performance-based bonuses.

• Relocation assistance may be available for candidates moving to Bangalore.

• Employee discounts on NetApp products and services. Working Hours:

• Standard full-time work week, typically 40 hours.

• Core business hours are expected, with flexibility often provided to accommodate project needs and personal schedules, subject to team and manager approval.

• Occasional requirement for extended hours or weekend work during critical project phases or release cycles.

📝 Enhancement Note: The salary range is an estimate based on market data for similar roles in Bangalore, considering the specific skill set and experience level. NetApp, as a large tech company, is expected to offer a competitive benefits package. The "IC - Typically requires a minimum of 4+ years of related experience" note in the original description has been interpreted to align with a mid-level engineer role.

🎯 Team & Company Context

🏢 Company Culture

Industry: Information Technology and Services, specifically Cloud Data Services and Storage Solutions. NetApp is a leader in enterprise-grade data management, storage, and cloud infrastructure solutions, serving a global clientele across various sectors.

Company Size: NetApp is a large enterprise, typically employing over 10,000 people globally. This size implies a structured environment with established processes, significant resources, and opportunities for career growth within a large organization.

Founded: NetApp was founded in 1992. With decades of experience, the company has a rich history of innovation and adaptation in the technology landscape, which likely translates to a culture that values both stability and forward-thinking.

Team Structure:

• The Quality Engineering team within NetApp is likely structured to support various product lines and engineering groups. Roles often involve specialized areas like test automation, performance testing, security testing, and now, AI/ML testing.

• Reporting structures typically involve leads or managers overseeing teams of engineers, with clear lines of communication to product management and development leadership.

• Cross-functional collaboration is essential, with Quality Engineers working closely with Software Developers, Product Managers, Site Reliability Engineers (SREs), and DevOps teams to ensure comprehensive quality assurance and efficient delivery pipelines. Methodology:

• NetApp likely employs Agile methodologies (Scrum, Kanban) for software development and testing, emphasizing iterative development, continuous feedback, and rapid adaptation.

• Data-driven decision-making is crucial, with a focus on metrics and analytics to measure product quality, test effectiveness, and release readiness.

• Emphasis on automation and efficiency is paramount, with a drive to automate repetitive tasks and streamline testing processes through CI/CD and intelligent tooling.

• A culture of continuous learning and innovation is fostered, particularly in areas like AI and cloud technologies, encouraging engineers to explore new tools and techniques.

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

📝 Enhancement Note: NetApp's standing as a major player in cloud data services suggests a culture that values technical excellence, innovation, and collaboration. The integration of AI/GenAI into their quality engineering platforms indicates a forward-looking approach, essential for candidates who are adaptable and eager to work with emerging technologies.

📈 Career & Growth Analysis

Operations Career Level: This role is classified as a mid-level Software Development Engineer in Test (SDET) or equivalent (Software Engineer 3). It requires a solid foundation in software testing principles, strong programming skills, and the ability to independently design and implement complex automation solutions. The scope includes not just execution but also strategy, framework development, and influencing engineering practices.

Reporting Structure: The engineer will likely report to a Test Engineering Manager or a Lead SDET within a specific product or platform team. They will collaborate closely with developers, product managers, and potentially architects on a day-to-day basis.

Operations Impact: The impact of this role is significant, directly influencing the quality, reliability, and release velocity of NetApp's advanced software products, including those incorporating AI and agentic systems. By building robust automation, the engineer will reduce critical bugs in production, accelerate time-to-market, and ensure a superior customer experience, which in turn supports NetApp's revenue and market position.

Growth Opportunities:

• Technical Specialization: Deepen expertise in AI/GenAI testing, LLM evaluation, agentic system validation, or advanced performance/security testing.

• Leadership Development: Progress to a Senior SDET role, leading complex automation initiatives, mentoring junior engineers, and contributing to architectural decisions in quality engineering.

• Cross-Functional Mobility: Transition into roles within core software development, platform engineering, SRE, or product management, leveraging a strong understanding of the product lifecycle.

• Architectural Contributions: Influence the design and adoption of next-generation testing platforms and strategies within NetApp.

• Industry Recognition: Contribute to open-source projects, publish technical articles, or speak at industry conferences, enhancing personal and company reputation.

📝 Enhancement Note: The growth path for an SDET at a company like NetApp is typically well-defined, moving from individual contributor to senior roles, and potentially into management or specialized architecture tracks. The emphasis on AI/GenAI provides a unique opportunity for specialization in a high-demand field.

🌐 Work Environment

Office Type: On-site position in Bangalore, India. This indicates a traditional office-based work environment, fostering in-person collaboration, team synergy, and direct mentorship opportunities.

Office Location(s): Bangalore, Karnataka, India. This location is a major technology hub in India, offering a vibrant ecosystem of tech talent and resources.

Workspace Context:

• The workspace is expected to be a collaborative office environment designed to facilitate team interaction and knowledge sharing among engineers, developers, and product teams.

• Access to modern development tools, high-performance computing resources, and robust network infrastructure necessary for complex software development and testing.

• Opportunities for informal discussions, brainstorming sessions, and pairing with colleagues to solve challenging technical problems.

• A culture that encourages engagement with colleagues from diverse backgrounds and expertise. Work Schedule:

• The standard work schedule will be full-time, aligned with typical business hours in India (Asia/Kolkata timezone).

• While on-site, there may be a degree of flexibility in start and end times, subject to team and manager approval, to balance project demands with personal needs.

• Adherence to project deadlines and release schedules may occasionally require adjusted working hours or participation in critical go-live activities.

📝 Enhancement Note: An on-site role in Bangalore suggests a dynamic, collaborative setting within a major tech hub. The environment is likely conducive to deep technical work and team-based problem-solving, with access to necessary infrastructure and resources.

📄 Application & Portfolio Review Process

Interview Process:

• Initial Screening: A recruiter or hiring manager will conduct an initial phone screen to assess basic qualifications, experience, and cultural fit.

• Technical Phone/Video Interview(s): Expect one or more technical interviews focusing on programming skills (e.g., coding challenges in Python/JavaScript), data structures, algorithms, and core testing concepts.

This may include API testing scenarios.

• On-Site/Virtual On-site Interviews: This stage typically involves a series of interviews covering:

  • System Design/Architecture: Designing automation frameworks or testing strategies for complex systems, potentially including AI components.
  • Coding & Debugging: Live coding exercises or debugging scenarios to assess problem-solving skills and proficiency in required languages.
  • Behavioral & Situational Questions: Assessing your approach to teamwork, problem-solving, handling challenges, and driving quality initiatives.
  • Portfolio Review: A dedicated session to walk through selected projects from your portfolio, explaining your role, the challenges, solutions, and impact.

• Hiring Manager Interview: A final conversation to discuss overall fit, career aspirations, and the specifics of the role and team.

Portfolio Review Tips:

• Curate Selectively: Choose 2-3 impactful projects that best showcase your skills in API testing, UI automation, and ideally, experience with AI/agentic systems.

• Focus on Impact: For each project, clearly articulate the problem you solved, the solution you implemented, the technologies used, and the measurable results (e.g., increased test coverage, reduced bug escape rate, faster release cycles).

• Highlight Framework Design: If you built an automation framework, explain the architecture, the design choices, and how it addressed specific challenges.

• Demonstrate AI/Agentic Experience: If applicable, showcase any work related to LLMs, RAG, or agentic workflows, detailing your approach to evaluating their correctness and reliability.

• Prepare for Technical Deep Dives: Be ready to discuss the technical details of your projects, including code structure, testing methodologies, and challenges encountered.

• Explain Your Role: Clearly define your contribution, especially if it was a team project.

Challenge Preparation:

• Coding Proficiency: Practice coding problems on platforms like LeetCode or HackerRank, focusing on Python and JavaScript, with an emphasis on data structures, algorithms, and clean code practices.

• API Testing Scenarios: Prepare to discuss how you would test different types of APIs (REST, GraphQL), including error handling, security, and performance aspects.

• Automation Framework Design: Think about how you would design a scalable, maintainable automation framework from scratch, considering different layers of testing and technology choices.

• AI/GenAI Testing Concepts: Familiarize yourself with common challenges in testing AI models (e.g., non-determinism, bias, hallucination) and potential evaluation strategies. Understand concepts like RAG and agentic workflows.

• CI/CD Integration: Be ready to explain how to integrate automated tests into CI/CD pipelines and the benefits of doing so.

• Behavioral Questions: Prepare STAR (Situation, Task, Action, Result) method responses for common behavioral questions related to teamwork, problem-solving, and initiative.

📝 Enhancement Note: The interview process is likely rigorous, typical for a mid-level engineering role at a major tech company. A well-prepared portfolio is critical, especially highlighting any experience with the advanced AI/GenAI testing requirements. Candidates should be ready to demonstrate both strong foundational testing skills and an aptitude for emerging technologies.

🛠 Tools & Technology Stack

Primary Tools:

• Programming Languages: Python, JavaScript, TypeScript.

• API Testing Tools/Frameworks: Pytest, Playwright API Testing, Postman, RestAssured.

• Web UI Automation Tools: Playwright, Cypress, Selenium WebDriver.

• Version Control: Git.

• CI/CD Tools: Jenkins, GitLab CI, GitHub Actions, Azure DevOps.

• Containerization: Docker.

• Orchestration: Kubernetes.

Analytics & Reporting:

• Test Reporting Frameworks: Allure Report, ExtentReports, or custom reporting solutions.

• Dashboarding Tools: Grafana, Kibana, or custom solutions for visualizing test results and system performance.

• Observability Tools: Tools like Datadog, Splunk, Prometheus, or ELK stack for monitoring and debugging distributed systems.

CRM & Automation:

• While not directly a CRM role, understanding how testing integrates with development workflows and ticket management systems (e.g., Jira) is implied.

• Integration Tools: Knowledge of APIs and system integrations is beneficial for connecting various testing and development tools.

AI/ML Specific (Preferred):

• LLM Platforms: OpenAI, Azure OpenAI, Anthropic.

• AI Orchestration Frameworks: LangChain, LangGraph, CrewAI, AutoGen, Semantic Kernel.

• Vector Databases: Pinecone, Weaviate, Milvus, ChromaDB.

• Cloud Platforms: AWS, Azure, GCP.

📝 Enhancement Note: This is a comprehensive list that reflects the cutting-edge nature of the role. Proficiency in Python/JavaScript for automation and familiarity with CI/CD pipelines are table stakes. The specific inclusion of AI/ML tools like LangChain, Vector Databases, and LLM platforms highlights the specialized requirements of this position.

👥 Team Culture & Values

Operations Values:

• Quality First: A deep-seated commitment to engineering high-quality, reliable, and performant software, driven by proactive quality assurance rather than reactive bug fixing.

• Innovation & Curiosity: A culture that encourages exploring new technologies, particularly in AI and cloud computing, and applying them to improve testing methodologies and product quality.

• Collaboration & Transparency: Open communication and strong teamwork across development, product, and QA functions, fostering a shared responsibility for product success.

• Efficiency & Automation: A drive to automate repetitive tasks, streamline processes, and continuously optimize workflows to accelerate delivery and reduce manual effort.

• Continuous Improvement: A mindset of always seeking ways to enhance processes, tools, and skills to deliver better results and adapt to evolving industry standards.

Collaboration Style:

• Cross-Functional Integration: Working closely with developers to ensure testability from the outset, and with product managers to understand requirements and user impact.

• Agile Methodologies: Embracing iterative development, participating in stand-ups, sprint planning, and retrospectives to ensure alignment and continuous feedback.

• Knowledge Sharing: Actively participating in team discussions, code reviews, and internal documentation to share best practices, insights, and learnings.

• Constructive Feedback: Providing and receiving feedback openly and constructively to drive improvements in code quality, test strategies, and team processes.

📝 Enhancement Note: The culture at NetApp, especially within its engineering teams, is likely to be results-oriented, technically driven, and collaborative. The emphasis on innovation is particularly relevant given the role's focus on AI technologies.

⚡ Challenges & Growth Opportunities

Challenges:

• Testing Non-Deterministic Systems: Developing effective strategies and metrics to test AI models and agentic systems, which can exhibit non-deterministic behavior and are harder to validate than traditional software.

• Keeping Pace with AI Evolution: Continuously learning and adapting to the rapid advancements in AI, LLMs, and related frameworks to ensure testing methodologies remain relevant and effective.

• Defining AI Quality Metrics: Establishing clear, measurable, and meaningful quality metrics for AI outputs, such as agent correctness, hallucination rates, and workflow reliability.

• Integrating Complex Systems: Ensuring seamless integration of diverse components, including APIs, UIs, and AI models, within a robust end-to-end testing framework.

• Scalability of AI Testing: Designing test infrastructure and strategies that can scale efficiently to handle the computational demands and data volumes associated with AI testing.

Learning & Development Opportunities:

• Specialized AI/ML Training: Access to internal or external training programs focused on AI/GenAI, LLMs, RAG, and agentic systems.

• Cloud Certifications: Opportunities to obtain certifications in AWS, Azure, or GCP, enhancing cloud-native testing capabilities.

• Advanced Automation Techniques: Training in cutting-edge automation tools, techniques, and best practices.

• Mentorship Programs: Opportunities to learn from senior engineers and architects within NetApp.

• Industry Conferences & Workshops: Participation in leading tech conferences to stay abreast of industry trends and network with peers.

📝 Enhancement Note: The primary challenges are inherent to testing cutting-edge AI technologies. The growth opportunities are aligned with developing specialized expertise in these high-demand areas, making this a valuable career step for ambitious engineers.

💡 Interview Preparation

Strategy Questions:

• "Describe your approach to building an automation framework from scratch for a complex distributed system. What are the key architectural considerations?" (Focus on modularity, scalability, maintainability, and integration points).

• "How would you design an automated testing strategy for an AI agent that interacts with multiple APIs and makes decisions based on LLM outputs? What specific metrics would you track?" (Highlight your understanding of AI evaluation, agent correctness, and workflow reliability).

• "Imagine a scenario where an LLM-based feature is producing inconsistent or inaccurate results in production. How would you investigate this, and what automated checks would you implement to prevent recurrence?" (Demonstrate your debugging process, understanding of LLM behavior, and RAG concepts). Company & Culture Questions:

• "What interests you about NetApp and this specific role, particularly the focus on AI and agentic systems?" (Research NetApp's AI initiatives and align your interests with their strategy).

• "Describe a time you had to influence engineering practices or drive adoption of new testing methodologies within a team. How did you approach it?" (Showcase your ability to drive change and collaborate effectively).

• "How do you stay current with emerging technologies in software testing and AI?" (Demonstrate a growth mindset and proactive learning). Portfolio Presentation Strategy:

• Start with the 'Why': Clearly articulate the business problem or technical challenge your project addressed.

• Detail Your Contribution: Explain your specific role and responsibilities within the project.

• Showcase Technical Depth: Walk through the architecture, key components, and technologies used. Be ready to explain design decisions.

• Highlight Process Improvements: Explain how your work improved efficiency, quality, or release velocity. Use specific metrics.

• For AI Projects: Emphasize your approach to evaluating AI outputs, handling non-determinism, and any specific metrics used for agent correctness or hallucination detection.

• Conclude with Impact: Summarize the overall success and business value of the project.

📝 Enhancement Note: Interview preparation should focus on demonstrating a blend of strong foundational SDET skills and a proactive, curious approach to new technologies like AI/GenAI. The portfolio is a critical component, so candidates must be prepared to present their work articulately and highlight relevant achievements.

📌 Application Steps

To apply for this Software Development Engineer in Test position:

• Visit the NetApp careers portal and submit your application through the provided link: https://jobs.netapp.com/job/Bangalore%2C-Karnataka-Software-Development-Engineer-in-Test-%28API%2C-UI-&-Agentic-Systems%29/1421468400/

• Customize Your Resume: Tailor your resume to highlight experience with Python, JavaScript/TypeScript, API testing (Pytest, Playwright), UI automation (Playwright, Cypress), CI/CD, Git, and any exposure to AI/GenAI, LLMs, RAG, or agentic systems. Quantify achievements with metrics wherever possible.

• Prepare Your Portfolio: Select 2-3 key projects that best demonstrate your skills in framework design, API/UI automation, and ideally, your experience or understanding of testing AI-driven systems. Be ready to present these with a focus on problem, solution, technology, and impact.

• Practice Technical Skills: Refresh your knowledge of data structures, algorithms, and object-oriented programming. Practice coding challenges in Python and JavaScript. Be prepared for system design questions related to test automation architectures.

• Research NetApp: Understand NetApp's products, services, and its strategic direction, particularly concerning cloud data services and AI initiatives. Familiarize yourself with their company values and culture.

⚠️ Important Notice: This enhanced job description includes AI-generated insights and operations industry-standard assumptions. All details should be verified directly with the hiring organization before making application decisions.