IWD IS A SAAS COMPANY THAT HELPS BRANDS MANAGE THEIR MERCHANDISING CYCLE.
We provide collaborative solutions for brands to efficiently and instantly execute their in-store merchandising operations, improve the customer experience, and considerably increase sales.
Our software empower brands to achieve consistency in all point of sale and boost the performance of their merchandising strategies.
The opportunity
Most companies are still trying to figure out what AI means for them. We know what we want, we just need someone exceptional to build it.
This is a senior, hands-on role to design, build, and embed AI across our business. Not research. Not strategy decks. Real, working systems that change how our teams operate every day.
You'll sit across Product and Engineering, with a direct line to the CTO and CEO. You'll have the authority and the mandate to drive change across Sales, Customer Success, Engineering, and Ops.
We’ve made an intentional decision to build AI capability from scratch, with the right person leading it. That means genuine greenfield opportunity.
What you'll own
This is a 50/50 role: half hands-on builder, half standards-setter and change driver.
On the building side:
– Design and build AI-powered agents that automate and enhance internal workflows across Sales, CSM, Engineering, and Ops
– Connect our systems, data, and tooling to enable real automation
– Rapidly prototype solutions, validate what works, and formalise it into something scalable
– Set up and configure tools across our stack (Notion, Slack, Claude, agent frameworks)
On the standards and adoption side:
– Establish best practices for prompt design, agent architecture, and knowledge management
– Build and maintain a structured internal knowledge base that powers agents and decision-making
– Define how AI is built, used, and governed across the business
– Drive adoption, you don't just hand things off, you make sure they stick
What success looks like at 6–12 months
– A working ecosystem of AI agents actively used daily across Sales, CSM, Engineering, and Ops
– Clear, measurable reduction in manual effort across key workflows
– A structured, scalable knowledge base powering agent behaviour and team decisions
– Defined standards for how AI is built and maintained, a foundation the whole company can build on
The stack
The tools and languages you'll work with day-to-day:
Core language: Python
AI & automation: Claude, agent frameworks, LLM APIs
Integration layer: MCP (Model Context Protocol), REST APIs
Workflow & knowledge: Notion, Slack
Data Warehouse: Snowflake, Elastic Search, MariaDB
You'll work primarily in Python, building agents and integrations that connect our internal systems via APIs and MCP. We use Claude as our core AI layer and expect you to push the boundaries of what's possible with it.
The environment
We'd rather be honest about where we are than oversell it:
– Low current AI maturity, which means you'll define the standard, not inherit someone else's decisions
– Processes that are still forming, you'll bring structure where it's needed, and move fast where it isn't
– Tooling that's ready to be connected, the building blocks are there, they just need the right person to tie them together
If you want to build something from scratch and put your fingerprints on how a whole company works, this is exactly the role for you.
How you'll work
– You start with a working prototype, not a strategy document
– You prove value fast, then formalise what works
– You push for real adoption; you're not done when it's built
– You hold a high bar for quality and bring others up to it
What we're looking for
We care far more about what you've built than how you got here. That said, you'll likely bring:
– Strong engineering capability, you build, not just configure
– Proven experience with LLMs, AI tools, or agent-based systems in a production context
– Familiarity with modern AI workflow tools (Claude, Notion, Slack integrations, agent frameworks)
– The ability to move fast, prototype with intent, and then bring structure to what works
– Confidence operating in an environment with room to grow, you see low process as latitude, not friction
– A track record of driving adoption across teams, not just shipping features
If you've built and shipped real AI-driven workflows that are actually used by teams, not just prototypes or demos, we want to talk.
HR interview 30-45 minutes
1 to 1 CTO
1 to 1 Engineering director
Fit call - Founder
