Principal Engineer, AI Architect

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