AI/ML Engineer

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