Principal Engineer AI Architect

Company Description

👋🏼We're Nagarro.

We are a Digital Product Engineering company that is scaling in a big way! We build products, services, and experiences that inspire, excite, and delight. We work at scale across all devices and digital mediums, and our people exist everywhere in the world (18000+ experts across 40 countries, to be exact). Our work culture is dynamic and non-hierarchical. We're looking for great new colleagues. That's where you come in!

Job Description

REQUIREMENTS:

• Total experience 11+ years.
• Should have 10+ years of experience in software engineering, with strong depth in Python.
• Should have proven experience architecting and delivering production-grade Generative AI applications at scale.
• Should have deep understanding of LLM integration patterns, RAG systems, and AI-driven UX design.
• Should have strong system design skills across backend, frontend, and AI infrastructure layers.
• Must have experience defining technical strategy and influencing architecture across teams or pods.
• Should have hands-on experience with cloud platforms (AWS, Azure, or GCP) and distributed systems.
• Should have strong grasp of security, privacy, and governance considerations for enterprise AI.
• Must have ability to translate ambiguous business problems into durable technical architectures.
• Should have excellent communication skills, with the ability to influence senior stakeholders and technical leadership.
  
  

• Understanding the client’s business use cases and technical requirements and be able to convert them into technical design which elegantly meets the requirements.
• Own the architecture and technical vision for AI-powered, user-facing applications built with Python, React, and Generative AI.
• Design scalable, secure, and cost-efficient backend platforms for LLM inference, RAG pipelines, and agent-based orchestration.
• Define frontend architecture and UX patterns for AI-native applications, including conversational interfaces, copilots, and intelligent dashboards.
• Lead the design and implementation of complex GenAI workflows that combine LLMs, tools, APIs, structured data, and user context.
• Establish engineering standards and best practices for prompt design, model integration, evaluation, and observability.
• Drive GenAI platformisation—building reusable components, SDKs, and frameworks used across multiple teams or products.
• Partner with product, design, data, and business leaders to translate strategic objectives into scalable technical solutions.
• Review critical designs and codebases, unblock teams on complex technical challenges, and raise the overall engineering bar.
• Lead technical discovery and solutioning for high-impact initiatives, including client or executive-facing workshops when required.
• Ensure enterprise readiness: security, privacy, compliance, governance, and responsible AI practices.
• Use AI-assisted development tools (e.g., Copilot, Claude Code) to accelerate delivery while maintaining production-grade quality.
• Mapping decisions with requirements and be able to translate the same to developers.
• Identifying different solutions and being able to narrow down the best option that meets the client’s requirements.
• Defining guidelines and benchmarks for NFR considerations during project implementation
• Writing and reviewing design document explaining overall architecture, framework, and high-level design of the application for the developers
• Reviewing architecture and design on various aspects like extensibility, scalability, security, design patterns, user experience, NFRs, etc., and ensure that all relevant best practices are followed.
• Developing and designing the overall solution for defined functional and non-functional requirements; and defining technologies, patterns, and frameworks to materialize it
• Understanding and relating technology integration scenarios and applying these learnings in projects
• Resolving issues that are raised during code/review, through exhaustive systematic analysis of the root cause, and being able to justify the decision taken.
• Carrying out POCs to make sure that suggested design/technologies meet the requirements.