Extensive experience designing and building scalable AI/ML platforms or infrastructure in a production environment
Proven track record of applying LLMs and AI models to real-world product features and user-facing solutions
Deep expertise in backend engineering, distributed systems, and cloud-native technologies (e.g., Kubernetes, Docker, AWS/GCP/Azure)
Proven experience integrating and managing multiple LLMs and AI models, with a strong understanding of their operational requirements and limitations
Proficiency in orchestration frameworks and workflow engines (e.g., LangGraph, Airflow, Kubeflow, Ray, or similar)
Strong programming skills in Python, Go, TypeScript or similar languages used for backend and AI platform development
Experience with MLOps best practices, including model deployment, monitoring, logging, and automated evaluation
Demonstrated ability to address AI privacy and security challenges, including data anonymization and compliance with data protection regulations
Familiarity with search technologies and their integration into AI-driven applications
Excellent collaboration and communication skills, with a track record of working effectively in cross-functional teams
Passion for staying at the forefront of AI infrastructure and applying new technologies to solve real-world problems at scale
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