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Tier-1 brand and metro location increase competition, but seniority and niche AI/LLM SRE skills moderate applicant density.
Role requires deep SRE and specialized AI/LLM platform experience plus regulatory knowledge, limiting cross-industry transferability.
Explicit 8+ years and extensive mandatory SRE, cloud, and AI/LLM stack requirements raise shortlisting strictness.
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Lead deployment and operation of large-scale AI platform infrastructure including AI inference services, RAG pipelines, and AI serving platforms across multi-cloud environments.
Collaborate with research, engineering, and product teams to implement production-ready AI capabilities embedding ethical AI principles such as fairness, transparency, and accountability.
Own platform reliability, performance optimization, CI/CD pipelines, disaster recovery, and operational workflows to ensure high availability and cost-efficient AI/ML service delivery.
8+ years experience in SRE, DevOps, or Platform Engineering with large-scale systems.
Hands-on experience with AI inference services, Kubernetes, cloud platforms (AWS/Azure/GCP), Infrastructure as Code, CI/CD, observability, and cloud-native deployment.
Experience supporting production AI/ML, Generative AI, LLM, or data-intensive platforms with operational excellence including incident management and root cause analysis.
Work Experience Required: 8+ years in relevant engineering roles (SRE/DevOps/Platform Engineering).
Experienced in operating and scaling production AI/ML platforms with knowledge of MLOps/LLMOps, distributed systems, and ethical AI implementation.
Skilled in multi-region/multi-cluster cloud environments, platform cost optimization, and compliance with governance, security, and regulatory frameworks like GDPR or HIPAA.
Proficient in collaboration with cross-functional teams including AI/ML, Data Engineering, Security, demonstrating ownership of reliability and platform services from implementation through operations.