Platform Product Owner-AI Enablement & Vendor Experience

Overview

Drive the delivery of agentic AI across the platform, from identifying use cases and ensuring data foundations are in place through to building, launching, and tracking the value they create.

What you'll do

• Partner with use case owners and Global Process Leads to identify, qualify, and size AI opportunities across enterprise workflows.
• Maintain a sequenced AI use case pipeline aligned to the AI Transformation roadmap and OP priorities.
• Define data requirements (sources, fields, quality thresholds) needed for each agent to function reliably, and own Data Catalogue entries as a mandatory gate before POC or build.
• Build and maintain semantic search and retrieval layers (vector databases, embedding pipelines) to support agent grounding.
• Co-own technical design of AI agents with architects and engineering managers; define APIs, data flows, and integration patterns.
• Design and build RAG pipelines, multi-agent workflows, and orchestration logic using frameworks such as LangChain, LangGraph, LlamaIndex, or AutoGen.
• Fine-tune prompts and evaluate outputs across LLMs (GPT, Claude, Gemini, or open-source models) for business scenarios.
• Deploy AI solutions on cloud platforms (Azure, AWS, or GCP), applying responsible AI and governance practices throughout.
• Own business readiness for each launch (stakeholder alignment, training, communication) and mitigate adoption risks early.
• Define and track business KPIs (e.g. hours saved, error reduction) and technical KPIs (e.g. accuracy, latency) per agent, reporting value realisation to the Head of AI Enablement.

What you'll need

• Experience in product ownership, business analysis, or process improvement within a procurement, supply chain, or operations environment.
• Demonstrable hands-on daily use of AI tools (Claude, Copilot, ChatGPT or equivalent) to solve real work problems.
• Working proficiency in Python and SQL, sufficient to prototype and iterate on agent code, not just specify it.
• Comfortable reading datasets, writing basic SQL, and assessing quality without needing to be a data engineer.

Nice to have

• Deeper agentic AI experience (multi-agent systems, tool use, prompt engineering) with at least a working prototype built.
• Experience with Databricks, Snowflake, Spark, or similar data platforms, and deploying AI workloads on major cloud providers.
• Hands-on experience with at least one AI orchestration framework (LangChain, LangGraph, LlamaIndex, or AutoGen) and practical understanding of RAG, embeddings, and vector databases.
• Familiarity with data catalog/lineage concepts, MCP (Model Context Protocol), or transformation/platform product roles in a large matrixed organisation.
• Experience with procurement processes and platforms (e.g. Source-to-Contract, Procure-to-Pay, or vendor management systems).

Details

• Location: Mumbai, India.