Principal Engineer, AI Architect

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Principal Engineer, AI Architect based in India.

This is a senior technical leadership role focused on designing and scaling production-grade Generative AI solutions for complex enterprise <environments.You>’ll shape the architecture and technical vision of AI-powered applications spanning backend, frontend, cloud infrastructure, and intelligent user experiences.The role combines deep hands-on engineering with strategic influence across teams, products, and client <engagements.You>’ll work extensively with Python, React, LLMs, RAG architectures, AI agents, distributed systems, and cloud platforms.A key focus will be turning ambiguous business challenges into secure, scalable, cost-efficient, and durable technical <architectures.You>’ll also help establish engineering standards, reusable AI platforms, responsible AI practices, and high-quality development patterns.This opportunity is ideal for an experienced architect who enjoys solving complex problems, influencing technical direction, and raising the engineering bar across an organization.

Accountabilities

• Own the overall architecture and technical vision for AI-powered, user-facing applications built with Python, React, and Generative AI.
• Translate client business objectives, functional requirements, and technical constraints into elegant, scalable, and durable technical designs.
• Design scalable, secure, and cost-efficient backend platforms supporting LLM inference, RAG pipelines, and agent-based orchestration.
• Define frontend architecture and AI-native UX patterns for conversational interfaces, copilots, intelligent dashboards, and other AI-powered experiences.
• Lead the architecture and implementation of complex GenAI workflows combining LLMs, tools, APIs, structured data, and user context.
• Establish engineering standards and best practices covering prompt engineering, model integration, evaluation, observability, and AI-assisted development.
• Drive GenAI platformization by creating reusable components, SDKs, frameworks, and architectural patterns that can be leveraged across multiple teams and products.
• Partner with Product, Design, Data, Engineering, and business leaders to translate strategic objectives into scalable technical solutions.
• Review critical architectures, technical designs, and codebases, providing guidance on extensibility, scalability, security, design patterns, UX, and non-functional requirements.
• Define technical strategies and influence architecture decisions across teams, pods, and major initiatives.
• Lead technical discovery, solutioning, proof-of-concept activities, and client or executive-facing technical workshops when required.
• Ensure enterprise AI solutions meet security, privacy, compliance, governance, and responsible AI requirements.
• Define guidelines, benchmarks, and standards for non-functional requirements throughout project implementation.
• Produce and review architecture and high-level design documentation, clearly communicating technical decisions and implementation guidance to development teams.
• Evaluate alternative technical solutions and select approaches that best balance business requirements, scalability, performance, security, and cost.
• Resolve complex technical issues through systematic root-cause analysis and clearly communicate and justify architectural decisions.
• Use AI-assisted development tools such as GitHub Copilot or Claude Code to accelerate delivery while maintaining production-grade engineering standards.

Requirements

• 11+ years of total professional experience, including 10+ years in software engineering.
• Strong depth and hands-on expertise in Python and modern software engineering practices.
• Proven experience architecting and delivering production-grade Generative AI applications at scale.
• Deep understanding of LLM integration patterns, Retrieval-Augmented Generation (RAG), agentic architectures, and AI-driven user experiences.
• Strong system design capabilities across backend services, frontend applications, AI infrastructure, and distributed systems.
• Experience with Python and React in production application environments.
• Hands-on experience with major cloud platforms such as AWS, Azure, or GCP.
• Strong understanding of distributed systems, scalability, reliability, and cloud-native architecture.
• Experience defining technical strategy and influencing architecture across multiple engineering teams or pods.
• Strong understanding of enterprise AI security, privacy, compliance, governance, and responsible AI practices.
• Ability to translate ambiguous business problems into practical, scalable, and maintainable technical architectures.
• Strong understanding of non-functional requirements, including performance, scalability, security, extensibility, reliability, and cost optimization.
• Experience creating and reviewing architecture documents, high-level designs, technical guidelines, and engineering standards.
• Ability to conduct POCs and evaluate new technologies to validate architectural approaches.
• Strong analytical and problem-solving skills, including systematic root-cause analysis of complex technical issues.
• Excellent communication and stakeholder-management skills, with the ability to influence senior technical leaders, developers, clients, and business stakeholders.
• Bachelor’s or master’s degree in Computer Science, Information Technology, or a related field.

Benefits

• Opportunity to work on complex, enterprise-scale Generative AI and digital engineering initiatives.
• Exposure to modern technologies across Python, React, LLMs, RAG, AI agents, cloud platforms, and distributed systems.
• Senior-level technical ownership and the opportunity to influence architecture and engineering strategy.
• Collaboration with multidisciplinary teams across product, design, data, engineering, and business functions.
• Opportunities to work directly with senior stakeholders and participate in high-impact technical discovery and solutioning.
• Dynamic, collaborative, and non-hierarchical work environment.
• Opportunity to contribute to reusable AI platforms, frameworks, and engineering standards used across multiple initiatives.
• Continuous exposure to emerging AI technologies, tools, and engineering prac