Product Engineer - AI

About Nadifor

Nadifor helps companies grow their people as AI changes the way they work. We build Nadifor Grow, a platform for personalized learning, AI coaching, and learning operations, and run the Nadifor AI Program to help people apply AI in their roles. Our goal is to connect learning and practice to measurable improvements in what people can do.

We are an early-stage company, which means the product, engineering culture, and operating model are still being shaped. We are looking for people who want to do more than write code. We want engineers who care deeply about the product, the customer, the craft, and the company we are building.

Our engineering culture is inspired by strong product engineering teams: small teams, high ownership, direct customer context, fast iteration, and engineers who are expected to make thoughtful product and technical decisions.

The role

We are looking for an AI Product Engineer who enjoys building backend systems and turning AI capabilities into useful product experiences.

You will help build the systems behind Nadifor’s AI coaching, in-course tutoring, course creation, and learning workflows. This includes backend services, agent workflows, knowledge retrieval, evaluation loops, integrations, permissions, and automation that make these experiences reliable and useful for customers.

This is not a research ML role. We are looking for someone who can build production-grade AI product systems: pragmatic, reliable, understandable, and grounded in customer value.

You should be comfortable working across the stack, partnering closely with product-minded engineers, founders, and customers, and making both product and technical decisions.

Experience and scope

We’re looking for someone who can independently take a customer problem through to a reliable production feature. You’ll join our four-person engineering team, working directly with the CTO to shape the product and our technical decisions.

What you’ll do

• Build and improve backend and AI product systems, taking ownership of features or broader systems depending on your experience.
• Turn AI prototypes and customer problems into reliable production features.
• Design APIs, services, data models, jobs, workflows, and integrations.
• Build systems for LLM orchestration, retrieval, evaluation, observability, and feedback loops.
• Improve the reliability, explainability, and quality of AI-powered product behavior.
• Work on multi-tenant SaaS concerns such as permissions, auditability, data separation, and enterprise readiness.
• Partner with other product engineers to ship complete customer-facing features.
• Make pragmatic architecture decisions that balance speed, correctness, and future scale.
• Help define engineering practices around testing, monitoring, deployment, and operational excellence.
• Share knowledge and contribute to the team’s engineering practices. More experienced engineers will also mentor teammates and help guide technical decisions.

What we’re looking for

• Experience building backend software, with deeper production and operational experience expected at more senior levels.
• Strong TypeScript and/or Python experience, or the ability to become highly productive in either quickly.
• Solid backend engineering fundamentals, including APIs, services, and data models.
• Practical experience building with LLMs or AI product systems.
• Good judgment around reliability, observability, testing, and operational tradeoffs.
• Ability to work full-stack when needed, even if your strongest spike is backend/platform.
• Strong product judgment: you care about whether the system actually solves the customer problem.
• Comfort with ambiguity and early-stage startup pace.
• Ability to make thoughtful tradeoffs without over-engineering.
• Strong written and verbal communication.
• A sense of ownership: you follow through, communicate clearly, and ask for help when you need it.

Nice to have

• Experience in early-stage startups.
• Experience building B2B SaaS or enterprise software.
• Experience with RAG, evals, prompt/version management, LLM observability, agentic workflows, or human-in-the-loop systems.
• Experience with cloud infrastructure, DevOps, security, or compliance-heavy environments.
• Experience working directly with customers or customer-facing teams.

What success looks like

In your first months, you will have shipped useful improvements to our backend or AI product systems, built an understanding of our customers and architecture, and contributed to product quality and reliability. The scope of those contributions will reflect your experience.

Over time, you will take on broader ownership and help shape how AI product engineering works at Nadifor.