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Remote role and known employer raise visibility, but senior GenAI specialization limits applicant pool.
Requires deep GenAI, LLM integration, and cloud architecture experience so cross-industry transferability is limited.
Explicit 11+ years, principal-level GenAI architecture, cloud, LLM, RAG and vector DB requirements make filters strict.
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Architect and deliver production-grade Generative AI applications and scalable backend platforms for LLM inference, RAG pipelines, and agent-based orchestration.
Drive integration of AI/ML capabilities into products, define frontend architecture for AI-native applications, and lead design of complex GenAI workflows combining LLMs, APIs, and user context.
Lead technical strategy, set engineering standards, ensure enterprise readiness, and manage delivery across multiple pods building scalable AI and API services.
11+ years total experience with 10+ years in software engineering focusing on Python and React.
Proven experience in architecting and delivering Generative AI applications at scale, including ML, agentic AI models, RAG systems, and LLM integration patterns.
Hands-on experience with cloud platforms (AWS, Azure, or GCP) and distributed systems.
Bachelor’s or master’s degree in computer science, Information Technology, or related field.
Experienced in defining technical strategy and influencing multi-team architectures in agile environments for AI products.
Strong expertise in end-to-end AI product development including backend deployment, security reviews, QA, and integration of vector DB pipelines.
Ability to translate ambiguous business problems into scalable, secure technical architectures with focus on enterprise AI governance and responsible AI practices.