• Drive AI-related sales for F5 across the region • Identify and develop opportunities with customers investing in GPUs/DPUs • Target strategic AI initiatives across the region • Build and expand strategic relationships with ecosystem players • Work closely with internal teams • Maintain a strong pipeline of AI-related opportunities • Represent F5 at key AI summits and events

• Design, develop, and deliver scalable multi-tenant SaaS applications using .NET, Angular, SQL Server/PostgreSQL, and AWS. • Own end-to-end delivery of features, from domain modeling and backend services through frontend implementation, automated testing, deployment, and production support. • Generate production-ready code using AI-assisted development tools such as GitHub Copilot, Cursor, Claude Code, and Replit-based workflows. • Collaborate with Architects to define application architecture and validate technical approaches through Proof of Concepts (PoCs). • Design systems using modern architectural patterns including: Domain-Driven Design (DDD) CQRS Event-Driven Architecture Backend-for-Frontend (BFF) API Gateway • Build modern Angular applications using standalone components, Signals, RxJS, Reactive Forms, and reusable UI architectures. • Design efficient database models and optimize SQL performance for high-volume SaaS applications. • Implement scalable integration, messaging, and distributed system patterns. • Troubleshoot complex production issues, perform root cause analysis, and continuously improve system reliability and performance. • Mentor engineers through architecture guidance, technical leadership, code reviews, and knowledge sharing. • Drive engineering best practices around quality, automation, security, observability, and AI-powered software development.

• Refine developer, user, and agent journeys: Understand how developers, enterprise platform teams, partners, and customers, and their respective agents, consume NVIDIA AI software, then craft clear technical journeys supported by documentation, code examples, demos, and deployment guidance. • Showcase enterprise AI software workflows: Build demos, reference examples, notebooks, and sample applications that show how NVIDIA AI software components work together across model development, inference, RAG, agentic AI, evaluation, deployment, and operations. • Build compelling technical assets: Accelerate adoption by creating public-facing content such as product documentation, deployment guides, reference architectures, tutorials, blog posts, whitepapers, technical presentations, webinars, demo videos, and code examples. • Develop automation and docs-as-code workflows: Create repeatable examples and publishing workflows using Git-based documentation, CI/CD, scripts, templates, and AI-assisted docs or skills where appropriate. • Enable the field and partner ecosystem: Support solution architects, sales teams, cloud partners, ISVs, and ecosystem teams with technical assets that help them explain, deploy, and integrate NVIDIA enterprise AI software. • Collaborate across the stack: Work closely with Technical Marketing Engineering, Product Management, Engineering, Developer Relations, Field, and Marketing teams to turn product capabilities into practical adoption paths. • Capture feedback and improve the product experience: Use customer, partner, developer, and field feedback to identify gaps in usability, examples, documentation, deployment patterns, and product workflows. • Engage the developer and open source community: Advocate for NVIDIA AI software in developer, cloud-native, and open source ecosystems, encouraging adoption through clear examples and practical technical storytelling.

Role Description We are seeking an enthusiastic and motivated Associate Software Engineer to join our Master Data Management (MDM) product line within Precisely’s Data Integrity Suite. In this role, you will help build the backend systems that support Precisely’s next-generation MDM platform. You will work on enterprise-grade applications using Java, Python, and Angular to develop APIs, services, and data layers that manage trusted, high-quality enterprise data across integrations, workflows, and analytics. As part of an AI-first engineering organization, you will use AI tools as a core part of designing, building, testing, and operating software. You will be expected to apply AI to accelerate development, improve code quality, and strengthen problem-solving. You will work closely with engineers, architects, and product teams to deliver scalable, reliable, production-ready backend capabilities that address real customer needs. What you will do: - Design, develop, and maintain backend services using Java, Spring Boot, and REST APIs - Contribute to data models, API contracts, and service integrations for MDM workflows - Build systems supporting data ingestion, transformation, governance, and integration pipelines (aligned to MDM/DI ecosystem) - Use AI-powered tools to accelerate coding, debugging, testing, and design workflows - Implement clean architecture patterns (controller, service, repository) with strong separation of concerns - Implement structured logging and observability patterns (SLF4J, MDC context, correlation IDs) to enable production debugging and monitoring - Write high-quality unit and integration tests with proper mocking strategies to ensure code quality and maintainability - Debug and optimize performance, scalability, and reliability of backend systems - Apply observability best practices (logging, tracing, correlation IDs) to support production monitoring - Participate in Agile ceremonies, code reviews, and CI/CD practices - Collaborate cross-functionally with frontend, QA, product, and platform teams - Continuously learn and evaluate new technologies, especially in AI/agentic workflows and backend platforms Qualifications - Bachelor’s degree in computer science or related field - 1–2 years of experience in software development - Strong understanding of Java and object-oriented programming - Understanding of API design and application architecture - Basic knowledge of relational or NoSQL databases (SQL Server, Oracle, MongoDB, etc.) - Test-driven development mindset with understanding of unit test patterns, mocking strategies, and test quality gates - Understanding of concurrent programming fundamentals, including thread-safety contracts, immutability, and shared state management - Familiarity with async/reactive patterns (CompletableFuture, async services, event-driven workflows) - Strong problem-solving and communication skills AI Skills/Knowledge - Hands-on experience using AI tools for coding, debugging, and productivity (Copilot, Claude, ChatGPT, etc.) - Ability to evaluate and validate AI-generated code for correctness and performance - Interest in applying AI to data platforms, APIs, and backend workflows - Eagerness to adopt AI-assisted development practices as part of daily work Preferred Skills (a plus but not required) - Experience with Spring Boot, Hibernate, or microservices architecture - Exposure to cloud platforms (AWS, Azure) and distributed systems - Database knowledge (SQL/NoSQL) - Understanding of data platforms, MDM concepts, or data integration pipelines - Basic frontend awareness - Structured logging and observability (SLF4J, Logback, MDC context, correlation IDs, Datadog integration) - Event-driven architecture patterns, messaging standards (CloudEvents, async workflows, dead-letter routing) - Data mapping patterns and DTO design - ORM and query optimization (Hibernate, fetch strategies, lazy loading, pagination, N+1 query avoidance) - Defensive programming practices (null safety, input validation, guard clauses, failure atomicity) - Basic knowledge of Python or AI/ML concepts - Familiarity with CI/CD pipelines and DevOps practices