Director, Product Management

Salesforce Salesforce · Enterprise · Bangalore, India, India

Director of Product Management to lead next-generation AI initiatives and drive the strategy of AI-first, collaboration-first, and platform-centric innovations across Employee Service portfolio (ITSM and HRSD). Role involves building and scaling enterprise-grade Agentic AI systems, integrating LLMs, defining autonomous workflows, and translating industry trends into product features.

What you'd actually do

  1. Define Product Strategy: Own the product vision and strategy for AI-driven capabilities within Employee Service (IT and HR domains), ensuring alignment with broader business goals.
  2. Agentic AI Leadership: Lead the conceptualization and delivery of Agentic AI workflows, enabling autonomous, task-oriented AI agents to resolve internal IT tickets and HR requests.
  3. Architectural Strategy: Drive product and architectural decisions around LLM integration, RAG pipelines, and agent orchestration frameworks. You will own the trade-offs between model performance, latency, cost, and context window optimization.
  4. AI Trust, Safety & Evals: Design robust evaluation frameworks for AI agents to measure accuracy, mitigate hallucinations, manage prompt injection risks, and maintain enterprise-grade data privacy.
  5. Customer Discovery & Integration: Engage directly with enterprise customers to understand complex pain points, specifically focusing on how they discover, adopt, and integrate AI applications into their internal IT, HR, and custom enterprise ecosystems (e.g., via API-based integrations and secure server environments).

Skills

Required

  • 14+ years of product management experience in enterprise SaaS
  • demonstrated track record of taking an Agentic product from 0-to-1 and scaling it in a production environment
  • Hands-on familiarity with LLM orchestration (e.g., LangChain, LlamaIndex)
  • embedding models
  • vector databases
  • designing autonomous agent loops (planning, tool use, reflection)
  • deeply understand the trade-offs between different foundation models and fine-tuning vs. RAG approaches
  • Strong working knowledge of predictive ML models and data pipelines
  • Experience with AI evaluation techniques (e.g., LLM-as-a-judge)
  • continuous model improvement cycles based on human feedback
  • Ability to engage deeply with engineering and data science teams on architectural tradeoffs, model selection, and performance metrics
  • Proven track record of developing and executing comprehensive, multi-quarter product roadmaps that drive measurable business impact
  • Exceptional executive presence with the ability to navigate stakeholder alignment and articulate complex AI concepts to technical and non-technical audiences

Nice to have

  • Familiarity with Salesforce AI capa

What the JD emphasized

  • taking an Agentic product from 0-to-1 and scaling it in a production environment
  • LLM orchestration
  • autonomous agent loops
  • AI evaluation techniques
  • continuous model improvement cycles based on human feedback

Other signals

  • AI-first product strategy
  • autonomous agentic systems
  • LLM integration
  • RAG pipelines
  • agent orchestration