Role Overview

As a AI/ML Engineer at Rebtel you will define how AI is done at Rebtel.
What tooling we standardise on, how we evaluate models we put in front of real users, what "good" looks like for our prompts and our pipelines.
AI is going from a side project to the core of how Rebtel operates and what we ship to our users.
We're hiring the second engineer on our ML/AI subteam to help build it classical ML for the business, LLM-powered systems for the product, and a clear production mindset on both.
You'll sit inside the Data team, report to our Head of Data, and partner with one other ML/AI engineer to shape this capability from the ground up.
You'll own work across two complementary tracks, and you'll ship in both.
Classical ML, in production, for operational leverage
Risk and fraud models across payments, top-ups, and account behaviour
Churn prediction and retention modelling on a user base of a million-plus
Forecasting, pricing, segmentation, and the next batch of operational problems we haven't tackled yet
Owning models end-to-end scoping with stakeholders, building, deploying, monitoring, retraining LLMs and AI agents, from internal automation to in-product features
Start with our customer support agents and automations: RAG pipelines, prompt orchestration, tool-use, evaluation harnesses
Move LLM capability into the product as user-facing features
Help guide the company on where AI actually creates leverage, what to build, what to buy, what to ignore and turn the good ideas into shipped systems.

Requirements

4+ years of hands-on ML engineering in Python, with real production ownership not just notebooks
Strong fundamentals in classical ML: feature engineering, model selection, validation, and the unglamorous parts of keeping a model healthy in prod
A genuine production mindset: monitoring, retraining, eval harnesses, CI/CD for models, and the instinct to debug when something drifts at 2am
Comfort working the whole loop: stakeholder scoping → data → model → deployment → measurement → iteration
Strong written and spoken English, and the ability to translate between business problems and ML ones
Hands-on experience with LLM frameworks — LangChain, LangGraph, LlamaIndex, or equivalents
Built RAG systems, agentic workflows, or LLM-backed products that real users (or real internal teams) actually used
Comfortable with vector databases, embeddings, prompt evaluation, and measuring LLM systems with something more rigorous than vibes
Obsession with staying up-to-date on developments in the AI space
Experience with a major cloud (AWS / GCP / Azure), containers, and a modern data stack
Experience in MLOps tooling: MLflow, Airflow, Kubernetes and feature stores is a plus
Background in fintech, payments, telecom, or other regulated / operational domains is a plus.

Tech Stack

Airflow
AWS
Azure
Cloud
Google Cloud Platform
Kubernetes
Python

Benefits

Pension Plan
Health Checkups, Influenza shots and Private Medical Insurance
Dental Insurance
Occupational insurance
Wellness allowance (5,000 SEK)
Discount on gym memberships
Bonus program
Extra parental pay
30 days annual vacation
Monday breakfasts
Relocation Support

Rebtel

Open Positions (1)

Data

AI/ML Engineer