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.
• Define guardrails for security, privacy, intellectual property, licensing, data handling, and human oversight when using AI tools.
• Evaluate emerging AI development tools and recommend their adoption where they provide measurable engineering value.
• Measure the impact of AI-assisted development on delivery speed, quality, test coverage, maintainability, and developer experience.

Engineering and Quality Standards

• Set standards for architecture, APIs, authentication and authorization, observability, testing, CI/CD, configuration, and deployment.
• Establish expectations for automated unit, integration, API, contract, and end-to-end testing.
• Partner with QA and engineering teams to improve testability, regression coverage, and production confidence.
• Promote design-for-quality practices, including validation, resilience, diagnosability, performance, and backward compatibility.
• Run architecture reviews, critical code reviews, and technical design discussions.
• Define and promote engineering practices that reduce defects and prevent the accumulation of technical debt.

Technical Leadership

• Drive technical alignment across teams and resolve cross-team dependencies.
• Communicate architectural decisions, trade-offs, constraints, and risks to both technical and non-technical stakeholders.
• Mentor engineers and technical leads in application design, cloud architecture, so…