GenAI Product Builder

Job Title: GenAI Product Builder

Location: Bengaluru, India

Type: Full-time, On-site

Experience: 3-6 years

Reports To: Head of Product / CPTO

About the Organization:

We are an AI-driven franchising and licensing partner helping global consumer brands expand across Asia. We take ownership of the full value chain from sourcing and purchasing inventory to marketing, distribution, and after-sales service across India, GCC, and Southeast Asia.

Our portfolio includes 20+ international brands such as Puma, Hasbro, Nautica, French Connection (FCUK), and U.S. Polo Assn

We operate across major ecommerce platforms including Amazon, Flipkart, Noon, Lazada, TikTok, and Shopify D2C stores.

The business is already operational and scaling. The next big step is to build an AI layer on top of these operations to automate repetitive work, improve efficiency, and scale without increasing headcount linearly. This role will be part of the early team shaping that transformation.

The Role:

This is not a software engineering role. This is not a traditional product management role. This is a new kind of role for a new kind of company.

You are a product-minded builder who uses GenAI and low-code/no-code tools to solve real business problems. Youll embed directly with functional teams purchasing, merchandising, marketing, content, supply chain to observe their daily workflows, identify high-leverage opportunities for AI intervention, and build working prototypes using real data that deliver real outcomes.

Your prototypes dont live in sandboxes. They run against live data, get tested by actual brand managers and category leads, and produce measurable results. But they dont need to be production-grade youre not building fault-tolerant systems or managing infrastructure. You validate the idea, prove the value, and hand off a well-documented spec to the engineering team to productionize

Think of This Role As:

- A sharp PM who can build: You dont just write PRDs. You prototype working solutions yourself using GenAI tools.

- An embedded AI consultant: You sit inside operations teams, shadow their work, and identify where AI creates 10x leverage.

- A bridge from chaos to product: You turn messy, Excel-and-Slack-driven workflows into structured, testable AI-assisted processes.

What You Will Do?

1. Embed, Observe, Identify (25% of time):

- Sit with functional teams for days at a time. Watch how a purchasing manager decides reorder quantities. Observe how a merchandiser sets prices across 5 marketplaces. Understand how a content team localizes listings for UAE vs India. Map these workflows end-to-end, identify the repetitive, high-effort, low-creativity tasks, and define the job to be done for an AI agent.

- Example: You shadow the purchasing team and discover they spend 4 hours/day in Excel cross-referencing sell-through rates, warehouse stock levels, and supplier MOQs to generate purchase orders. This is a clear candidate for an AI-assisted buying copilot.

2. Prototype with Real Data (40% of time):

- Build working prototypes using GenAI tools, low-code platforms, and simple API integrations. Your prototypes use real company data - actual sales numbers, real inventory files, live marketplace listings - not mock datasets. The goal is a functional demo that operations teams can actually use and evaluate.

- Example: You build a Pricing Recommendation Agent using Claude + n8n that pulls competitor prices from marketplace APIs, checks MAP compliance rules, and suggests price adjustments. The purchasing head tests it on 3 brands for a week and confirms it reduces pricing decision time by 60%.

3. Test, Iterate, Validate (20% of time):

- Run lightweight experiments with real users. Collect feedback not just does it work? but do they trust it? do they use it? does it actually improve the metric? Iterate fast. Kill what doesnt work. Double down on what does.

- Example: Your inventory allocation prototype shows promising results on 2 brands, but category managers dont trust it for high-value SKUs. You add an explainability layer showing the reasoning behind each recommendation, and adoption jumps from 30% to 75%.

4. Package & Hand Off to Engineering (15% of time):

For validated POCs, prepare structured handoff documentation: workflow diagrams, prompt templates, data sources, guardrails, integration points, and success metrics. Collaborate with the engineering team to translate your prototype into a scalable, production-grade system

Key Outcomes in the First 6 Months:

Agent-Led Use Case Discovery:

- Identify and prioritize 8-10 high-impact AI use cases across purchasing, pricing, merchandising, content, and marketing functions

- Create agent use-case canvases with defined workflows, prompts, data sources, guardrails, and measurable outcomes

- Define jobs to be done for agents (e.g., Purchase Order Copilot, Dynamic Pricing Advisor, Content Localizer, Assortment Gap Finder)

Working Prototypes with Real Impact:

- Deliver 4-6 working prototypes tested with real data and real operations teams

- At least 2 prototypes validated and handed off to engineering for productionization

- Measurable impact on at least one key metric: days of inventory, pricing decision time, content turnaround, or headcount efficiency

Experimentation & Feedback Loops:

- Run lightweight tests (manual A/Bs, sandbox trials) with category managers and operations leads

- Build a prioritization framework: which tasks are high-effort, low-creativity, and high-frequency enough to warrant AI intervention

- Track Time to POC - Time to Value metrics to inform rollout sequencing.

Key Competencies:

GenAI Product Thinking:

- Deep familiarity with LLMs, prompt engineering, and agent design patterns (tool use, multi-step reasoning, human-in-the-loop)

- Strong judgment on when to use GenAI vs. rules-based automation vs. a simple spreadsheet formula

- Can scope a workflow and determine where an AI agent adds genuine value vs. where its over-engineering

Ecommerce Operations Understanding:

- Working knowledge of how purchasing, pricing, merchandising, content, and marketing decisions happen in ecommerce businesses

- Understands metrics like sell-through rate, days of inventory, AOV, conversion, ROAS, and margin

- Familiarity with how marketplaces (Amazon, Flipkart, Noon, Lazada) work - catalog structures, pricing rules, advertising models.

Rapid Prototyping Skills:

- Can stitch together a working prototype in days using tools like Claude, n8n, Zapier, Make, Streamlit, Retool, or Replit

- Comfortable connecting LLM APIs, marketplace data feeds, Google Sheets/Airtable, and simple REST APIs into a workflow

- Can create clear visual workflows using Figma, Miro, or Notion to communicate agent logic to non-technical stakeholders

Communication & Stakeholder Management:

- Able to tell a clear story: This is the problem, this is what the agent does, this is the impact, this is what we need to productionize it

- Comfortable presenting to non-technical business leaders and collecting structured feedback from operations teams

- Partners cross-functionally with category, operations, and engineering teams.

Tools You May Use:

- LLM APIs such as Claude, GPT-4, and Gemini

- Agent frameworks such as LangChain, LangGraph, CrewAI, Flowise

- Low-code tools like n8n, Zapier, Make, Retool, Streamlit, Replit

- Basic REST APIs, Google Sheets, Airtable, CSV files, and simple SQL

- Collaboration tools such as Slack, Notion, Jira, and Google Workspace

- Deep ML research, heavy infrastructure management, DevOps, Kubernetes, or advanced MLOps experience is not required.

Qualifications/Must Have:

- 3-6 years of experience in product, ecommerce operations, category management, growth, or business operations.

- Hands-on experience building GenAI prototypes or MVPs.

- Ability to convert business problems into working demos using low-code/no-code tools.

- Strong understanding of ecommerce operations.

- Experience collaborating with engineering teams to productionize solutions.

- Excellent communication skills.

Strong Preference For:

- Experience in ecommerce marketplaces or growth-stage startups.

- Experience building GenAI tools adopted by real users.

- Cross-functional roles such as technical PM, solutions consultant, product engineer, or implementation lead.

- Exposure to cross-border ecommerce markets like India, GCC, or Southeast Asia