Technical Architect – Application Design & AI Engineering

Job Description: Location: Hyderabad Employment Type: Full-time Experience: 12-15 years Primary Areas: Application Architecture, Enterprise & Cloud Architecture, AI-Assisted Software Engineering, Quality Engineering Primary Stack: Python/Django, Go/Gin, Angular/TypeScript, MySQL, PostgreSQL, Redis, Azure, Docker, Terraform, Playwright ROLE SUMMARY We are looking for a hands-on Technical Architect to define and evolve the application architecture of Nextpower's engineering and business platforms. The role will focus on designing scalable, maintainable, and secure applications across the full technology stack, including domain models, APIs, services, user interfaces, data stores, integrations, and deployment environments. The architect will also establish practical guidelines for AI-assisted software development, helping teams use AI tools to improve productivity while maintaining strong standards for design quality, security, testing, maintainability, and responsible use. This is a senior individual-contributor role that works closely with product management, engineering, QA, DevOps, security, and subject-matter experts. The role remains hands-on in complex or high-risk areas through design reviews, prototypes, reference implementations, and critical code reviews. KEY RESPONSIBILITIES Application Architecture and Design Define target application architectures, reference architectures, and reusable design patterns. Design end-to-end solutions covering domain models, business services, APIs, user interfaces, persistence, integrations, security, and deployment. Establish standards for modular application design, service boundaries, API contracts, error handling, configuration, and versioning. Review major application designs for functional suitability, scalability, reliability, security, maintainability, and operational supportability. Guide teams in selecting appropriate architectural styles, including modular monoliths, microservices, event-driven components, and workflow-based solutions. Identify architectural risks, design inconsistencies, and technical debt, and drive practical remediation plans. Remain hands-on through prototypes, proof-of-concepts, reference implementations, and targeted development in uncertain or high-risk areas. AI-Assisted Software Engineering Define guidelines for the responsible and effective use of AI coding assistants and generative AI tools across the software development lifecycle. Establish recommended practices for AI-assisted requirements analysis, architecture, coding, testing, documentation, code review, and troubleshooting. Define quality gates and review expectations for AI-generated or AI-assisted code. Develop reusable prompts, templates, coding patterns, architectural context, and development workflows that improve consistency and engineering productivity. Guide teams in using AI to generate and maintain unit tests, API tests, UI tests, documentation, migration scripts, and technical designs.