WHO WE'RE LOOKING FOR

We are looking for a Machine Learning Engineer to build, deploy, and improve ML systems that power casino recommendations across our platform. This role focuses on taking models from experimentation to production, working closely with backend, data, and data science teams, and ensuring reliable, low-latency inference in Python-based services within a multi-tenant environment. You will help shape model quality, Airflow-based feature and training pipelines, and end-to-end monitoring so recommendation outputs are accurate, scalable, and measurable in business impact.

AS A PART OF OUR TEAM YOU WILL

Support and improve the current recommendation system solution in a multi-tenant environment
Drive model and pipeline performance improvements across training and inference workloads
Build and maintain end-to-end ML pipelines: training, validation, deployment, and retraining
Integrate inference into backend services with attention to latency, reliability, performance, and memory management
Define and track model performance metrics tied to product and business outcomes
Improve data quality and feature availability in partnership with data and backend teams
Set up monitoring for model health, drift, and production incidents; drive remediation
Run controlled experiments and analyze results to guide model and product improvements
Document model behavior, assumptions, and operational runbooks for maintainability
Contribute to architecture and technical decisions for scalable ML infrastructure

WHAT WE EXPECT

Experience and education

5+ years of experience in ML engineering
A degree in a quantitative field (e.g., Mathematics, Statistics, Computer Science)

Core skills

Strong Python experience for production ML services
Solid ML fundamentals (supervised learning, ranking/recommendation, model evaluation)
Experience with feature engineering and data preprocessing for behavioral/event data
Hands-on model deployment experience (APIs, batch jobs, Airflow, CI/CD, containerized environments)
Familiarity with SQL and data-access patterns for analytics and operational datasets
Experience monitoring the whole ML pipeline, including model and data quality (drift, performance decay, alerting)
Understanding of statistics and experimentation (A/B testing, experiment design)
Ability to collaborate with backend engineers on scalable service integration
Strong software engineering practices (testing, code reviews, documentation)
Strong computer science fundamentals (operating systems, processes, memory management)

Nice to have

Experience with multi-tenant systems; Kubernetes (K8s)

WHAT THE HIRING PROCESS LOOKS LIKE

Application (15-30 minutes): Our Recruitment team will get familiar with your experience and skills and provide feedback on our decision regarding your application
Preliminary Call (15-30 minutes): Preliminary call serves as the first opportunity for us to learn more about your background, technical skills, and to answer any questions you might have about the role or company
Technical Interview (1 hr 30 mins): You’ll have a deep-dive discussion focused on your practical experience in data science and machine learning.
Soft-skill Interview (45-60 minutes): The last stage discussion centers around your fit with the company culture, your career ambitions and alignment with our team’s goals.
Offer Presentation (30-45 minutes): Once we've confirmed you're the right fit for the role, we'll prepare a job offer and present it to you. This includes all the details about your role, compensation, and the next steps to join our team.

The decision-making time between stages at our company typically spans 3 to 5 business days. However, some interviews or time intervals between interviews for decision-making may take more or less time than indicated, depending on the position, the candidate's specific experience, or other unforeseen circumstances. We are committed to maintaining a transparent and respectful hiring process, ensuring that all candidates are evaluated fairly and equitably. Additionally, we encourage candidates to ask questions at any stage of the process to clarify any concerns or requirements.

OUR BENEFITS

Wellness program

Medical compensation
Paid sick leaves
Compensation for sports activities
Well-being webinars and workshops

Work & life balance

Wellness Day: 4th Friday off monthly
Remote work
21 working days of vacation
5 personal days per year

Professional development

English speaking club
Language learning bonus €150 per month
80% paid professional employee training
Provided tech equipment

Extra advantages

€150 for the arrangement of the workplace
Bonuses for significant events and additional personal days if necessary
Offline and online company parties and team buildings

WHY WORK WITH US?

Joining us means becoming a part of a company that prioritizes steady progress, efficiency and innovation. Our growth is consistent and thoughtful. We avoid sharp leaps in employee expansion, as we’re striving to ensure the correct establishment of processes and smooth development. This approach allows us to maintain a stable environment and makes our company a standout place to advance your career in the iGaming industry.