ML Engineer
At a glance
Mid-level Machine Learning Engineering role at Toogeza. Europe · full-time.
Pay not stated ML Engineer salary
aimlroles summary, based on the employer's posting.
What you'll do
- Build, train and put probability-of-default models and credit-limit approaches into use
- Find modelling opportunities in lending, then develop fraud, collections and conversion improvements
- Prepare datasets and create useful features for modelling work
- Maintain model records, monitor performance and run A/B tests
- Create computer-vision workflows and LLM tools for lending operations or customer experience
What you bring
- At least seven years in machine learning or data science, including three years in credit lending
- You have delivered and productionised credit-risk models or strategies, such as default prediction and credit limits
- Strong Python and SQL skills, plus a sound command of ML methods, feature creation and model evaluation
- You understand online lending workflows and can source, clean and prepare data for modelling
- Intermediate English or higher
Who this fits
This mid-level listing describes a senior machine-learning role focused on credit lending. It suits someone who can take lending models from development through production and testing. The role also includes computer-vision and LLM projects.
From the employer
We are toogeza, a Ukrainian recruiting company that is focused on hiring talents and building teams for tech startups worldwide. People make a difference in the big game, and we may help to find the right ones.
Currently, we are looking for an experienced Senior ML Engineer to join one of toogeza’s clients.
Professional qualifications
7+ years’ experience in Machine Learning / Data Science, with 3+ years in credit-lending organisations.
Demonstrated delivery and productionisation of Probability-of-Default (PD) models, credit-limit strategies, fraud-detection, conversion-uplift, and collections-optimisation models.
Advanced Python proficiency and solid grasp of modern ML algorithms, feature engineering, and model-evaluation best practices.
Ability to write, structure, and optimise complex SQL queries.
Deep understanding of the credit lifecycle, especially online lending workflows.
Proven skill in sourcing, cleansing, and generating features from data sets.
Comfortable setting up and maintaining modelling environments (local, cloud, or on-prem).
Detail-oriented, accountable, and committed to both team and individual targets.
English: Intermediate (B1) or higher.
Preferred / bonus qualifications
Practical experience with LLM solutions:
Using commercial APIs (e.g., OpenAI, Anthropic, etc.).
Self-hosting of open-source models
Fine-tuning of open-source models.
Building voice chatbots.
Building RAG chatbots.
Experience with Computer Vision models for document or image processing.
Building ML pipelines and deploying models to production.
Creating executive dashboards and model reports in Power BI.
Main responsibilities
Design, train, and deploy probability of default models.
Build credit-limit strategies.
Discover and scope AI/ML opportunities that boost efficiency and revenue of the company, including collections optimisation, fraud control, conversion lift, etc.
Analyse data sources and engineer features for modelling.
Produce and update internal model documentation.
Implement model monitoring.
Plan and execute A/B tests.
Build Computer Vision pipelines to automate lending workflows.
Develop LLM-based solutions that streamline internal processes or enhance customer experience.
Expected results
Implemented probability of default models and credit-limit strategies.
Launched A/B tests for models that potentially can boost the efficiency and/or revenue of the company.
Thorough, audit-ready documentation for models.
What We Offer
Join a fast-scaling FinTech company where your decisions shape the business and your contributions truly matter.
Enjoy 20 paid days off annually, flexible scheduling, and a supportive, people-first culture.
Partial compensation for medical insurance, sports activities, and foreign language.
Work in an international, agile team with ambitious goals, modern tools, and a strong sense of purpose.
Professional qualifications
7+ years’ experience in Machine Learning / Data Science, with 3+ years in credit-lending organisations.
Demonstrated delivery and productionisation of Probability-of-Default (PD) models, credit-limit strategies, fraud-detection, conversion-uplift, and collections-optimisation models.
Advanced Python proficiency and solid grasp of modern ML algorithms, feature engineering, and model-evaluation best practices.
Ability to write, structure, and optimise complex SQL queries.
Deep understanding of the credit lifecycle, especially online lending workflows.
Proven skill in sourcing, cleansing, and generating features from data sets.
Comfortable setting up and maintaining modelling environments (local, cloud, or on-prem).
Detail-oriented, accountable, and committed to both team and individual targets.
English: Intermediate (B1) or higher.
Preferred / bonus qualifications
Practical experience with LLM solutions:
Using commercial APIs (e.g., OpenAI, Anthropic, etc.).
Self-hosting of open-source models
Fine-tuning of open-source models.
Building voice chatbots.
Building RAG chatbots.
Experience with Computer Vision models for document or image processing.
Building ML pipelines and deploying models to production.
Creating executive dashboards and model reports in Power BI.
Main responsibilities
Design, train, and deploy probability of default models.
Build credit-limit strategies.
Discover and scope AI/ML opportunities that boost efficiency and revenue of the company, including collections optimisation, fraud control, conversion lift, etc.
Analyse data sources and engineer features for modelling.
Produce and update internal model documentation.
Implement model monitoring.
Plan and execute A/B tests.
Build Computer Vision pipelines to automate lending workflows.
Develop LLM-based solutions that streamline internal processes or enhance customer experience.
Expected results
Implemented probability of default models and credit-limit strategies.
Launched A/B tests for models that potentially can boost the efficiency and/or revenue of the company.
Thorough, audit-ready documentation for models.
What We Offer
Join a fast-scaling FinTech company where your decisions shape the business and your contributions truly matter.
Enjoy 20 paid days off annually, flexible scheduling, and a supportive, people-first culture.
Partial compensation for medical insurance, sports activities, and foreign language.
Work in an international, agile team with ambitious goals, modern tools, and a strong sense of purpose.