Senior Machine Learning Engineer - Fraud Detection

Modulai; Göteborgs kommun; On-site; SEK 600,000 - 800,000; Full time

Job summary

A tech firm specializing in machine learning is seeking a Senior Machine Learning Engineer to work on a cutting-edge fraud detection platform. The role emphasizes collaboration with engineers, data scientists, and risk teams to develop robust ML models. Ideal candidates will possess strong Python skills, a solid understanding of machine learning fundamentals, and experience with MLOps tools. This full-time position is a unique opportunity to impact the fintech sector through advanced data-driven solutions.

Qualifications

• Strong fundamentals in machine learning and statistics.
• Hands-on experience with ML pipelines and model monitoring.
• Ability to translate technical concepts for various stakeholders.

Responsibilities

• Design and deploy ML models for transaction-level fraud detection.
• Analyze large datasets to identify patterns and data quality issues.
• Collaborate with cross-functional teams to ensure model effectiveness.

Skills

Production-grade Python

XGBoost/LightGBM

Scikit-learn

Experience building ML APIs

MLOps

Education

MSc or Ph.D. in a quantitative field

Tools

FastAPI

Docker

PySpark

Job description

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 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!