AI/ML Engineer
RebTel Networks AB · Stockholm · Tills vidare
RebTel Networks AB is hiring an **AI/ML Engineer** based at its office in Stockholm. Joining the Data team as the second engineer on the ML/AI subteam and reporting to the Head of Data, the role involves establishing tooling standards and model evaluation across two main tracks. Responsibilities cover classical machine learning in production for operational needs—such as fraud, risk, churn prediction, pricing, and forecasting—as well as building LLM systems, RAG pipelines, and AI agents for internal customer support automation and user-facing product features. The position manages models end-to-end, spanning scoping, deployment, CI/CD, monitoring, and retraining. Applicants must have at least four years of **hands-on ML engineering in Python** with practical production ownership beyond notebooks. Requirements include strong fundamentals in classical ML, hands-on experience with LLM frameworks such as LangChain, LangGraph, LlamaIndex, or equivalents, and demonstrated work building RAG systems, agentic workflows, vector databases, or LLM-backed products. Candidates need experience with containers, modern data stacks, a major cloud provider (AWS, GCP, or Azure), and **strong written and spoken English**. Experience with MLOps tooling like MLflow, Airflow, Kubernetes, and feature stores, or a background in fintech, payments, or telecom, is considered a plus.
What will you do?
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.
Areas of ownership: 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:
- You are an excellent communicator and collaborator. We work in English, but you will hear many languages in our Stockholm office
- 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
Why Rebtel?
Rebtel has been connecting people across borders for nearly 20 years. Today, we’re profitable, growing, and at a pivotal moment in our journey. As we enter our next phase, we’re building an organisation designed for speed, ownership, and real impact, where every role contributes directly to shaping what comes next.
This is a place with global ambition and a strong foundation, where ideas move quickly and decisions matter. You won’t get lost in layers of process or slow-moving structures. Instead, you’ll find the space to take ownership, collaborate across teams, and make meaningful contributions from day one. Based in Stockholm, we bring together a diverse, international team united by a shared purpose: to simplify the way people connect worldwide.
At Rebtel, you are the most important asset and we strive to provide a comprehensive package of benefits and perks that enhance your well-being and work experience. Here are some of the things you can expect from us:
- 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, if you're joining us from afar, we'll assist you in making a smooth transition.
We are Rebtel. We come from all around the world to create products for anyone who has crossed a border. We believe in equal opportunity and value diversity at our company. We do not discriminate on the basis of race, religion, color, national origin, gender, sexual orientation, age, marital status, veteran status, or disability status.