FREE ACCESS
5,000–10,000 jobs/day
See all jobs on JobTailor
Search thousands of fresh jobs every day.
Discover
- Fresh listings
- Fast filters
- No subscription required
Create a free account and start exploring right away.
Tech Stack
Tools & technologiesAirflowAWSAzureCloudGoogle Cloud PlatformKubernetesPython
About the role
Key responsibilities & impact- 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
What you’ll need- 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.
Benefits
Comp & perks- 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
ATS Keywords
✓ Tailor your resumeApplicant Tracking System Keywords
Tip: use these terms in your resume and cover letter to boost ATS matches.
Hard Skills & Tools
machine learningPythonfeature engineeringmodel selectionmodel validationmonitoringretrainingLLM frameworksRAG systemsMLOps
Soft Skills
production mindsetstakeholder scopingdebuggingwritten communicationspoken communicationtranslating business problemsiteration