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
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