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Tier-1 brand, metro location, and broad GenAI/data skill requirements increase qualified applicant density.
High due to enterprise data architecture, governance, multi-tenant and regulated-data specialization requiring domain experience.
Mandatory 10+ years, specialized GenAI/data architecture, cloud and enterprise governance requirements create strict filters.
Own end-to-end architecture for enterprise-scale Generative AI solutions, including retrieval, orchestration, integration, and evaluation to ensure scalability and security.
Design and implement retrieval-augmented generation architectures using document ingestion, indexing, hybrid retrieval, and relevance tuning for production use.
Lead AI system evaluation standards, data governance enforcement, operational economics optimization, and act as principal technical authority for AI architecture in global teams.
Bachelor's degree in Computer Science or related technical field or equivalent experience.
10+ years of software engineering experience with significant focus on architecture and technical leadership of large-scale platforms.
Hands-on experience designing and delivering production Generative AI solutions including retrieval-augmented generation and multi-phase agent workflows.
Programming skills in backend languages (Python, Node.js/TypeScript), cloud-native Azure engineering experience with containerization, CI/CD, monitoring, and observability.
Experienced hands-on architect capable of independently designing end-to-end AI solutions and active in development and production problem-solving.
Strong expertise in distributed data systems design addressing failure, latency, duplication, and drift, especially in AI contexts.
Proven ability to lead global, cross-functional teams, influencing stakeholders without formal authority and translating business needs into scalable technical architectures.