Senior Software Architect

Broadcom · Hyderabad · 10+ yrs experience · Posted 2026-04-15

Tech stack: AWS, Angular, Azure, C++, Docker, GCP, GraphQL, Java, JavaScript, Jenkins, Kubernetes, MongoDB, MySQL, Node.js, PostgreSQL, Python, React, SQL, Spring Boot, TypeScript

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About the role

Customer-Focused Problem Solving with Practical AI Implementation We are seeking a Senior Architect with a strong QA mindset and hands-on engineering expertise to drive customer value through strategic application of Generative AI and Machine Learning technologies. This role requires a pragmatic approach to AI implementation—focusing on solving real customer problems and product pain points rather than superficial AI integration for marketing purposes. The ideal candidate will work across multiple enterprise DevOps product lines (Agile Requirements Designer, Service Virtualization, Test Data Manager, Nolio Release Automation and Continuous Delivery Director) to identify opportunities where AI/ML can deliver measurable business value, design and implement solutions, and ensure quality through comprehensive testing and validation. Responsibilities: - 1. Customer Problem Identification & Solution Design Analyze customer feedback, support tickets, and product usage data to identify pain points and opportunities for AI/ML intervention Conduct root cause analysis of product issues and design AI-powered solutions that address underlying problems Design end-to-end solutions that integrate AI/ML capabilities seamlessly into existing product workflows Create proof-of-concepts (POCs) to validate AI solutions before full implementation Measure and demonstrate ROI of AI implementations through quantifiable metrics (time savings, error reduction, user satisfaction) 2. Hands-On Development & Implementation Write production-quality code across multiple technology stacks (Java, Python, TypeScript, C++) Implement AI/ML models using frameworks like LangChain, LangGraph, OpenAI APIs, and custom ML pipelines Integrate AI capabilities into existing microservices and monolithic applications Build APIs and services that expose AI functionality to product features Develop data pipelines for training, inference, and model management Code reviews and technical leadership for AI/ML implementations 3. Quality Assurance & Testing Design comprehensive test strategies for AI/ML systems including: Unit tests for AI model wrappers and data processing Integration tests for AI service endpoints Performance and load testing for AI inference pipelines Accuracy and validation testing for model outputs A/B - testing frameworks for model comparison Implement automated testing for AI features to ensure reliability Validate AI outputs for correctness, bias, and edge cases Monitor AI system performance in production and establish alerting 4. Architecture & Technical Leadership Define AI/ML architecture patterns and best practices for the organization Create technical documentation for AI implementations Mentor engineers on AI/ML best practices and pragmatic implementation approaches Evaluate and select AI/ML tools and frameworks based on technical merit and business value Design scalable AI infrastructure that can handle production workloads Qualifications: - Core Programming Languages Java 17+ (Spring Boot 3.5+, microservices architecture) Python 3.10+ (AI/ML development, data processing) TypeScript/JavaScript (Angular 19, React, Node.js) SQL (complex queries, database optimization) - AI/ML Technologies & Frameworks Generative AI: LangChain and LangGraph for multi-agent workflows OpenAI API, Anthropic Claude, or similar LLM APIs Prompt engineering and optimization RAG (Retrieval-Augmented Generation) implementations Vector databases and embeddings - Machine Learning: Scikit-learn, pandas, NumPy for traditional ML Model training, evaluation, and deployment Feature engineering and data preprocessing Model versioning and MLOps practices - AI/ML Infrastructure: Model serving and inference pipelines API design for AI services Performance optimization for AI workloads Cost optimization for AI API usage - Enterprise Technology Stack - Backend Frameworks: Spring Boot 3.5+ (Java microservices) Grails 5.3+ (legacy system maintenance) RESTful APIs and GraphQL - Frontend Technologies: Angular 19+ (modern web applications) React (component-based UI) TypeScript, JavaScript (ES6+) Webpack, Vite, or modern build tools - Database & Data Management: PostgreSQL, MySQL, MSSQL, Oracle MongoDB (NoSQL) Database schema design and optimization Data migration and ETL processes - DevOps & Infrastructure: Docker and containerization CI/CD pipelines (Jenkins, GitLab CI) Gradle, Maven (build automation) Kubernetes (container orchestration - preferred) - Authentication & Security: Keycloak, OAuth2, JWT Security best practices for AI systems Data privacy and compliance (GDPR, PII handling) - Testing & Quality Assurance - Testing Frameworks: JUnit, TestNG (Java) pytest, unittest (Python) Jest, Vitest (TypeScript/JavaScript) Protractor, Selenium (E2E testing) - AI/ML Testing: Model validation and accuracy testing A/B - testing frameworks Performance benchmarking Bias detection and fairness testing - Additional Technologies (Nice to Have) C++/Qt (desktop application development) Groovy (build scripts, legacy systems) XML/XSLT (data transformation) GraphQL (API design) - Education & Experience Bachelor's degree in Computer Science, Engineering, or related field (Master's preferred) 10+ years of software development experience 3+ years of hands-on experience with AI/ML implementation in production systems 5+ years of experience with enterprise Java and Spring Boot Proven track record of solving customer problems with measurable business impact Strong QA mindset with experience in test-driven development (TDD) Experience with AI/ML frameworks (LangChain, LangGraph, scikit-learn, TensorFlow, or PyTorch) Proficiency in prompt engineering and LLM optimization Understanding of MLOps practices and model lifecycle management Experience with microservices architecture and distributed systems Strong database skills including complex queries and optimization Experience with cloud platforms (AWS, Azure, or GCP) preferred Soft Skills Customer-focused mindset - ability to translate customer pain points into technical solutions Pragmatic approach - focus on value delivery over technology for technology's sake Strong problem-solving skills - ability to analyze complex problems and design effective solutions Excellent communication - ability to explain technical concepts to non-technical stakeholders Collaborative - works effectively with cross-functional teams Self-directed - able to identify opportunities and drive initiatives independently Preferred Qualifications Experience with test data management or data generation systems Experience with requirements management or test automation tools Knowledge of PII detection and privacy compliance (GDPR, CCPA) Experience with synthetic data generation using AI Familiarity with enterprise software development lifecycle Experience with legacy system modernization and migration Contributions to open-source AI/ML projects Published papers or presentations on AI/ML topics What You'll Be Working On Product Lines ARD (Agile Requirements Designer) - Requirements management and test automation platform SV (Service Virtualization) - Service virtualization and API mocking platform TDM (Test Data Manager) - Test data management and synthetic data generation Nolio – Nolio Release Automation CDD – Continuous Delivery Director What We're NOT Looking For AI hype followers who want to add AI features just because it's trendy Theoretical researchers without practical implementation experience Developers who avoid testing or don't value quality assurance Solo contributors who can't work collaboratively Technology chasers who prioritize new tech over customer value What We ARE Looking For Pragmatic problem solvers who use AI/ML as a tool to solve real problems Quality-focused engineers who write tests and ensure reliability Customer advocates who understand user pain points and design solutions accordingly Hands-on architects who can both design and implement solutions Value creators who measure success by business i

Qualifications

- Core Programming Languages Java 17+ (Spring Boot 3.5+, microservices architecture) Python 3.10+ (AI/ML development, data processing) TypeScript/JavaScript (Angular 19, React, Node.js) SQL (complex queries, database optimization)
- AI/ML Technologies & Frameworks Generative AI: LangChain and LangGraph for multi-agent workflows OpenAI API, Anthropic Claude, or similar LLM APIs Prompt engineering and optimization RAG (Retrieval-Augmented Generation) implementations Vector databases and embeddings
- Machine Learning: Scikit-learn, pandas, NumPy for traditional ML Model training, evaluation, and deployment Feature engineering and data preprocessing Model versioning and MLOps practices
- AI/ML Infrastructure: Model serving and inference pipelines API design for AI services Performance optimization for AI workloads Cost optimization for AI API usage
- Enterprise Technology Stack
- Backend Frameworks: Spring Boot 3.5+ (Java microservices) Grails 5.3+ (legacy system maintenance) RESTful APIs and GraphQL
- Frontend Technologies: Angular 19+ (modern web applications) React (component-based UI) TypeScript, JavaScript (ES6+) Webpack, Vite, or modern build tools
- Database & Data Management: PostgreSQL, MySQL, MSSQL, Oracle MongoDB (NoSQL) Database schema design and optimization Data migration and ETL processes
- DevOps & Infrastructure: Docker and containerization CI/CD pipelines (Jenkins, GitLab CI) Gradle, Maven (build automation) Kubernetes (container orchestration - preferred)
- Authentication & Security: Keycloak, OAuth2, JWT Security best practices for AI systems Data privacy and compliance (GDPR, PII handling)
- Testing & Quality Assurance
- Testing Frameworks: JUnit, TestNG (Java) pytest, unittest (Python) Jest, Vitest (TypeScript/JavaScript) Protractor, Selenium (E2E testing)
- AI/ML Testing: Model validation and accuracy testing A/B testing frameworks Performance benchmarking Bias detection and fairness testing
- Additional Technologies (Nice to Have) C++/Qt (desktop application development) Groovy (build scripts, legacy systems) XML/XSLT (data transformation) GraphQL (API design)
- Education & Experience Bachelor's degree in Computer Science, Engineering, or related field (Master's preferred) 10+ years of software development experience 3+ years of hands-on experience with AI/ML implementation in production systems 5+ years of experience with enterprise Java and Spring Boot
- Proven track record of solving customer problems with measurable business impact
- Strong QA mindset with experience in test-driven development (TDD)
- Experience with AI/ML frameworks (LangChain, LangGraph, scikit-learn, TensorFlow, or PyTorch)
- Proficiency in prompt engineering and LLM optimization
- Understanding of MLOps practices and model lifecycle management
- Experience with microservices architecture and distributed systems
- Strong database skills including complex queries and optimization
- Experience with cloud platforms (AWS, Azure, or GCP) preferred Soft Skills Customer-focused mindset - ability to translate customer pain points into technical solutions Pragmatic approach - focus on value delivery over technology for technology's sake
- Strong problem-solving skills - ability to
- analyze complex problems and design effective solutions
- Excellent communication - ability to explain technical concepts to non-technical stakeholders Collaborative - works effectively with cross-functional teams Self-directed - able to identify opportunities and drive initiatives independently Preferred Qualifications
- Experience with test data management or data generation systems
- Experience with requirements management or test automation tools
- Knowledge of PII detection and privacy compliance (GDPR, CCPA)
- Experience with synthetic data generation using AI
- Familiarity with enterprise software development lifecycle
- Experience with legacy system modernization and migration Contributions to open-source AI/ML projects Published papers or presentations on AI/ML topics What You'll Be
- Working On Product Lines ARD (Agile Requirements Designer)
- Requirements management and test automation platform SV (Service Virtualization)
- Service virtualization and API mocking platform TDM (Test Data Manager)
- Test data management and synthetic data generation Nolio
- Nolio Release Automation CDD
- Continuous Delivery Director What We're NOT Looking For AI hype followers who want to add AI features just because it's trendy Theoretical researchers without practical implementation experience Developers who avoid testing or don't value quality assurance Solo contributors who can't work collaboratively Technology chasers who prioritize new tech over customer value What We ARE Looking For Pragmatic problem solvers who use AI/ML as a tool to solve real problems Quality-focused engineers who write tests and ensure reliability Customer advocates who understand user pain points and design solutions accordingly
- Hands-on architects who can both design and implement solutions Value creators who measure success by business i

Responsibilities

- Customer Problem Identification & Solution Design
- Analyze customer feedback, support tickets, and product usage data to identify pain points and opportunities for AI/ML intervention Conduct root cause analysis of product issues and design AI-powered solutions that address underlying problems Design end-to-end solutions that integrate AI/ML capabilities seamlessly into existing product workflows
- Create proof-of-concepts (POCs) to validate AI solutions before full implementation Measure and demonstrate ROI of AI implementations through quantifiable metrics (time savings, error reduction, user satisfaction)
- Hands-On Development & Implementation Write production-quality code across multiple technology stacks (Java, Python, TypeScript, C++) Implement AI/ML models using frameworks like LangChain, LangGraph, OpenAI APIs, and custom ML pipelines Integrate AI capabilities into existing microservices and monolithic applications
- Build APIs and services that expose AI functionality to product features
- Develop data pipelines for training, inference, and model management Code reviews and technical leadership for AI/ML implementations
- Quality Assurance & Testing Design comprehensive test strategies for AI/ML systems including: Unit tests for AI model wrappers and data processing Integration tests for AI service endpoints Performance and load testing for AI inference pipelines Accuracy and validation testing for model outputs A/B testing frameworks for model comparison Implement automated testing for AI features to ensure reliability Validate AI outputs for correctness, bias, and edge cases
- Monitor AI system performance in production and establish alerting
- Architecture & Technical Leadership
- Define AI/ML architecture patterns and best practices for the organization
- Create technical documentation for AI implementations Mentor engineers on AI/ML best practices and pragmatic implementation approaches Evaluate and select AI/ML tools and frameworks based on technical merit and business value
- Design scalable AI infrastructure that can handle production workloads

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