Senior Machine Learning Engineer - Fraud Detection
At a glance
Senior Machine Learning Engineering role at Modulai. Stockholm.
Pay not stated Machine Learning Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build and deploy models that flag fraudulent transactions, mainly using tabular data
- Inspect large transaction datasets for patterns, data quality problems, bias and leakage
- Create and maintain real-time and batch ML services, including pipelines, monitoring and experiment tools
- Work with client engineering, data, risk and compliance teams, explaining findings to technical and non-technical audiences
- Keep deployed models reliable, explainable, traceable and compliant
What you bring
- You have production-level Python skills and strong ML fundamentals, including feature engineering and imbalanced-data methods
- You have shipped ML APIs and worked with Docker, CI/CD and distributed data processing such as PySpark or SQL
- You bring hands-on MLOps experience with model monitoring, real-time inference, A/B testing and tools such as Airflow or Kubeflow
- You hold a master's or doctorate in a quantitative discipline
- You have worked in fraud, payments, fintech or credit risk, including models that affect business risk
Who this fits
This suits a senior, hands-on engineer who can take ML work from scoping through deployment and explain decisions clearly in English. You’ll work directly with client teams in a regulated financial setting and contribute as part of a team.
From the employer
SENIOR MACHINE LEARNING ENGINEER
We are recruiting Senior Machine Learning Engineers to work on the development of a next-generation fraud detection platform for a major Payment Service Provider (PSP).
The role combines production-grade machine learning engineering, advanced data analysis/statistics, and customer-facing technical collaboration. You will work closely with the client’s data, engineering, risk, and compliance teams to design, implement, deploy, and continuously improve real-time ML models operating in a highly regulated financial environment.
We approach these problems as a team, meaning that you will have to be able to clearly explain your reasoning and code in order to engage the rest of us. This is a hands-on, forward-deployed role requiring both deep technical expertise and strong communication skills in English.
Core Responsibilities
Design, train, evaluate, and deploy ML models for transaction-level fraud detection (primarily tabular data).
Analyze large-scale transaction datasets to identify patterns, leakage, bias, and data quality issues.
Build and maintain production ML services (real-time and batch).
Implement robust ML pipelines, model monitoring, and experiment frameworks.
Collaborate directly with client engineers, data scientists, and risk teams.
Translate complex technical concepts and results into clear, actionable insights for technical and non-technical stakeholders.
Operate within strict requirements for reliability, explainability, traceability, and compliance.
Background and skills:
Production-grade Python and solid ML fundamentals (XGBoost/LightGBM, Scikit-learn, feature engineering, imbalanced datasets)
Experience building and shipping ML-powered APIs (FastAPI/Flask), Docker, CI/CD, and distributed data processing (PySpark/SQL)
Strong stats foundation: experimental design, bias/leakage detection, time-dependent validation
Hands-on MLOps experience — feature stores, Airflow/Kubeflow, model monitoring, real-time inference, A/B testing
MSc or Ph.D. in a quantitative field
Excellent understanding of a broad set of ML algorithms and frameworks
A passion for lean, clean, and maintainable code
The desire to grow and to share insights with others
Domain experience: Fraud detection, payments, fintech, or credit risk. You've worked with cost-sensitive decisions, highly imbalanced data, and models that directly impact business risk.
How you work: You communicate clearly with engineers, product, and compliance stakeholders alike. You write good documentation and can hold your own in architecture discussions.
About Team Modulai
At Modulai, we focus 100% on solving problems with machine learning (ML). We work in teams on a project basis, for clients, as part of the core team in startups where we have long-term engagements, and we also build our own ML products.
Learning and teamwork are central to how we work. Everyone in the team is or will soon be a full-stack ML engineer capable of scoping and developing end-to-end ML solutions. You should be able to do end-to-end machine learning products by yourself but never do it because we always work in teams. If there is data, we will do ML on it!
Note:
Due to the summer holiday period, applications will be reviewed starting 27th of July.