Principal Engineer, AI Architect
New
J
JobgetherSecurity & IT
IndiaFull-TimePrincipal
Salary not disclosed
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Job Details
- Experience
- 11+ years of total professional experience, including 10+ years in software engineering.
- Required Skills
- AWSPythonGCPAzureReactGenerative AI
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.
- Bachelor’s or master’s degree in Computer Science, Information Technology, or a related field.
Responsibilities
- 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.
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