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Senior niche GenAI architect with MLOps and cloud requirements, moderate candidate competition.
Deep GenAI architecture and enterprise MLOps requirements create high background fit sensitivity.
Explicit 9+ years and mandatory GenAI, MLOps, cloud, and architecture skills create high shortlisting strictness.
Own the architecture and technical vision for AI-powered user-facing applications using Python, React, and Generative AI.
Design and implement scalable, secure backend platforms for LLM inference, RAG pipelines, and agent orchestration, including frontend AI-native UX patterns.
Lead GenAI platformization by building reusable components and frameworks, review critical designs, unblock teams, and drive engineering standards across multiple teams.
Total experience: 9+ years in software engineering.
Proven experience architecting and delivering production-grade Generative AI applications at scale.
Strong expertise in Python, microservices, APIs, system design, and MLOps tools like MLflow, Vertex AI, or Kubeflow.
Bachelor’s or Master’s degree in Computer Science, IT, or related field.
Experienced in multi-layer system design spanning backend, frontend, and AI infrastructure with cloud platforms, especially GCP and/or Databricks.
Demonstrated ability to define technical strategy and influence architecture across teams or pods, including translating ambiguous business problems into technical architectures.
Skilled in enterprise AI considerations such as security, privacy, governance, and responsible AI practices, capable of leading high-impact technical discoveries and workshops.