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Tier-1 brand and metro Bengaluru location increase applicant density despite niche LLM specialization.
Deep LLM infrastructure, vendor, and capacity expertise make skills less transferable across industries.
Explicit 10-12 years, hands-on coding, Python, SQL, and deep LLM infra make filters stringent.
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Own and operate automated systems for real-time monitoring and management of capacity across Salesforce's large-scale LLM fleet involving multiple 1st party and 3rd party models and providers.
Develop mathematical and statistical models to forecast capacity demand for trillion-token workloads, optimizing hyperscaler commitments and internal cluster sizing with latency and cost considerations.
Automate capacity decisions and economic tradeoffs by creating scripts, pipelines, and tooling for reservation management, rate limiting, failover, and managing capacity plans for enterprise anchor customers.
10 to 12 years of experience in technical or infrastructure product management or equivalent role involving building systems.
Proficiency in coding, specifically Python, with ability to write automation scripts and lightweight dashboards independently.
Strong data analysis and mathematical skills, including experience with telemetry data, SQL or Splunk, and building forecasting models.
Deep understanding of LLM infrastructure concepts such as tokens, TPM/RPM rate limits, PTU vs PayGo economics, latency, and operational experience managing production systems at scale.
Experienced in multi-provider LLM routing and capacity management tooling for large-scale, rate-sensitive workloads.
Familiarity with hyperscaler provisioning tradeoffs (e.g., Azure OpenAI, Google Vertex) and vendor engagement for commercial and technical decisions.
Background aligned with SRE, MLOps, or platform engineering enabling close collaboration with infrastructure engineers and building internal automation tools.