Senior Product Manager & AI Builder

About Oriient:

At Oriient, we are building the world’s most accurate Indoor GPS, and the only one that needs no hardware at all. We are a VC-backed company in a phase of hyper-growth with strong market traction, live in thousands of locations worldwide across retail, smart buildings, hospitals, campuses, and other large indoor environments.

On top of the positioning engine sits a thick agentic layer that turns location into intelligence. Every session feeds a dataset of indoor traffic and behavior at enormous volume, unmatched anywhere in the market, and a growing set of AI agents and LLM-based tools that surface insights from it: how people move, dwell, and shop inside physical spaces, delivered to our customers and to our own teams as answers, alerts, and recommendations rather than raw data.

Job Summary:

We are looking for a Senior Product Manager who is also a hands-on builder. Not a PM who only writes specs for someone else to implement, and not an engineer without product judgment. Someone who can find the problem, decide it is worth solving, build the first working version themselves, and then take it to real users.

You will take AI products and internal agents from problem to production, across the company. That means customer-facing AI capabilities built on our indoor data, and the internal agents and tools our own teams run on. The scope is deliberately broad: the problems worth solving will not always sit in the same department, and we are looking for someone who can pick up any of them without needing the domain handed to them.

In parallel, you will join an effort already underway to turn Oriient’s R&D into an agentic process, and build a meaningful part of it yourself. The goal is an order-of-magnitude change in how fast we move from an idea to production.

Key Responsibilities:

Own AI products end to end

• Own your part of the roadmap. Help define the product roadmap, decide what gets built in your area, in what order, and why, and make sure every effort ties back to a measurable outcome for a real user, whether that user is a customer or a colleague.
• Run your own discovery. Sit with the people whose work you are changing, find the friction yourself, and turn it into a prioritized set of problems rather than a queue of requests.
• Define what good means for AI products. Together with the engineers building them, set evaluation criteria, quality thresholds, and release gates for AI-based features, and keep watching them after launch.
• Know when to stop. Set go/no-go gates on exploratory work and make the call, with the team, to kill the efforts that do not pay off, early.

Build it yourself

• Ship working software. Use current models and agentic coding tools to build and ship agents, tools, and prototypes, sometimes on your own and sometimes alongside engineering. You are a builder, not only a spec writer.
• Make prototype-first the default. Turn a requirement into something that runs, then decide with the team whether it deserves to become a product. Raise the building bar of the whole product and R&D organization.
• Take things to production. Get from a prototype that works on your machine to a tool with real users, real data, and a clear owner, working with engineering on what needs to be productized properly.

Help make Oriient AI-native

This transformation is already underway and has people behind it. You will be a significant part of that effort, not the only one driving it.

• Help redesign how we go from idea to production. Together with Product and R&D leadership, rethink the development lifecycle around AI agents, with people where judgment is needed and agents everywhere else.
• Drive adoption and quality. Help take agents and tools from pilot to daily use across teams, with the guardrails that keep speed from costing quality or security.
• Measure velocity, not activity. Contribute to the metrics that show the transformation is working, and to reporting progress against an order-of-magnitude target.
• Spread the practice. Teach, pair, and publish, so that building with AI becomes the normal way people here work rather than something one team does.

Qualifications and Experience:

• 5+ years of product management experience on technical products, with a track record of shipping things that people actually use.
• Development background. You have written code professionally as an engineer, or you have a technical degree plus a real history of building. You can read a codebase, take part in an architecture discussion, and hold your own with senior engineers.
• Hands-on AI builder. You use current models and agentic coding tools every day to build working tools, agents, and prototypes, and you can show us what you built. Using AI to summarize documents or draft specs does not count.
• Shipped AI to production. At least one AI-based production grade capability with real business impact (LLM, ML, or computer vision) released to real users, where you can describe how quality was defined, measured, and maintained.
• Range across disciplines. Experience in more than one discipline (product, engineering, data, design, operations) and a track record of owning problems across different domains, teams, or functions. You get productive in an unfamiliar area quickly and do not need the problem pre-scoped for you.
• Analytical and creative in equal measure. You can read an evaluation, question a methodology, and reason from data. You can also invent the thing nobody asked for. This role needs both: the rigor to know whether something actually works, and the imagination to see what is worth building next.
• Product fundamentals. Discovery, prioritization, crisp requirements, metrics, and the ability to say no with a reason. Comfortable with data (SQL, notebooks, BI) to answer your own questions. Excellent written and verbal communication in English.

Advantages:

• Background as an engineer, data scientist, or technical founder before moving into product.
• Experience building with agent frameworks, MCP, evals, and multi-agent workflows, or running coding agents in CI/CD.
• Experience in developer experience, internal platforms, or engineering productivity tooling.
• A history of building internal tools that people adopted and kept using.
• Startup or high-growth experience, ideally where you were the first to bring AI-native practices into the team.