Senior Product Manager - LLM Fleet Management

Salesforce Salesforce · Enterprise · Bangalore, India

Product Manager for AI Infrastructure at Salesforce, focusing on LLM fleet management. This role involves building and operating automated systems for capacity intelligence, forecasting demand, automating capacity decisions, managing fleet economics, driving customer capacity, and defining the operating playbook for LLM capacity. The role requires hands-on coding, understanding of LLM infrastructure fundamentals, and operational instincts at scale.

What you'd actually do

  1. Own the capacity intelligence layer: Build & operate automated systems that monitor TPM/RPM utilization across our entire fleet of 1st party and 3rd party models, regions, and providers in real time, surfacing signals before they become incidents.
  2. Forecast and model capacity demand: Apply strong mathematical and statistical rigor to project trillion token workloads. You will build the financial and operational models that dictate our hyperscaler commitments and internal cluster sizing, balancing strict latency SLAs against complex cost structures.
  3. Automate capacity decisions: Write and maintain scripts and pipelines that trigger PTU reservations, rate limit adjustments, PayGo spillover rules, and provider-level failover based on usage patterns.
  4. Manage the fleet economics: Track model spend across our internal 1st party infrastructure and 3rd party providers (Azure/OpenAI, Google Gemini, Anthropic, and others), model by model, region by region. You will build tooling that dynamically optimizes reservations versus PayGo tradeoffs.
  5. Drive anchor customer capacity: Manage capacity plans for our largest and most complex customers by analyzing usage data, stress-testing projections, and building capacity buffers that prevent hard-kill scenarios at launch.

Skills

Required

  • Product Management
  • Infrastructure PM
  • Hands-on coding (Python)
  • AI agent fluency
  • Data analysis
  • Mathematical modeling
  • SQL
  • Splunk
  • LLM infrastructure fundamentals (tokens, TPM/RPM, PTU vs PayGo, context window, latency)
  • Operational experience with production systems
  • Vendor and partner engagement

Nice to have

  • Multi-provider LLM routing
  • Provisioned Throughput vs PayGo tradeoffs at hyperscalers
  • Capacity management tooling for ML inference or rate-sensitive workloads
  • SRE
  • MLOps
  • Platform engineering
  • Building internal tools (Slack bots, dashboards, automation apps)

What the JD emphasized

  • write code
  • build automation pipelines
  • instrument systems
  • hands-on coding
  • Python is the minimum bar
  • highly capable of leveraging coding agents
  • strong data and mathematical fluency
  • pull and interpret usage telemetry independently
  • apply solid math skills to build accurate forecasting models
  • deep understanding of LLM infrastructure fundamentals
  • operational instincts at scale
  • operating or supported production systems under pressure

Other signals

  • LLM fleet management
  • capacity intelligence
  • forecasting and modeling demand
  • automating capacity decisions
  • fleet economics
  • customer capacity management
  • operating playbook
  • system of record for capacity