AI Engineer

## About the role Softrip is transforming from a traditional SaaS travel technology company into an AI-first platform — not by bolting AI features onto existing software, but by rethinking what the product can do when AI is native to it. Of our existing customer base, we have beta customers ready to build with us, solid discovery work already done, and a clear direction. We need dedicated engineers with external experience doing exactly this to help us deliver — and to keep pushing the boundaries of what's possible as our customer base grows and our AI capabilities expand. You'll ship AI-powered features into both of our products — one serving enterprise travel companies, one serving SMB operators — and build the engineering layer underneath them. You'll be hired alongside an AI Product Manager as a deliberate two-person AI team. The working model is iterative and close: you're not receiving specs and building to them. You're figuring out what to build together, in real time, and shipping it. The foundation is in place: Langfuse is running for LLM observability and LaunchDarkly is configured so prompts can be updated and pushed to production without a code deploy. You'll build on top of that foundation and extend it as our capabilities and customer demands grow. The team is small by design. There's no dedicated design function, no QA layer, and no DevOps handoff. You own what you build end to end, from initial integration to production reliability. If your instinct is to wait for sign-off before making a call, this won't fit. If you've been the person who figures things out and ships them without a net, keep reading. ## Responsibilities - You ship AI features from week one. Not a proof of concept — features in the product that real beta customers interact with. You start focused, learn what the product and customers actually need from AI, and grow the complexity from there. - You build context pipelines. The model isn't the hard part — assembling the right product data at the right moment is. Getting customer and operational data assembled efficiently, within context limits, and in a format the model can reason over is real engineering work. You'll solve it per feature and get better at it with every iteration. - You build and own the eval harness. Logging is already running in Langfuse. You build the layer that makes it actionable: golden datasets of representative inputs and expected outputs, automated pipelines that run evals when prompts change, and metrics that surface quality problems before a customer does. - You handle data governance for AI. What customer data can go to external LLM APIs? Where does PII live, and how do you ensure it doesn't leave the system? These questions have real contractual and regulatory answers. The engineering implementation is yours. - You build toward RAG and MCP in parallel. Both are strategic