Machine Learning Engineer

Sportserve   forms part of a remarkable group of B2C sports betting and B2B sportsbook technology companies, focused on delivering first class sports betting experiences and casino products for our users worldwide.
Along with Sportion, TechSpirit, Standard Focus and Sportelligent, we are the driving force behind the world renowned sports betting company and our flagship brand, Dafabet.
Since launching our global hiring initiative, we now employ over 2,000 people worldwide, offering exciting career paths in Technology, Trading, Operations and Media.
We pride ourselves on having a diverse and international culture that embraces the global community and acts locally.
We offer office based, hybrid and remote work on permanent and consultancy contracts all over the world, making us the true global employer of choice.
We are looking for a Machine Learning Engineer to develop AI-powered applications specifically for our sportsbook platform.
This role involves designing, building, and deploying machine learning solutions that address key business challenges in real-time betting, predictive analytics, and customer engagement.
The ideal candidate will work both independently and within teams to drive ML development, maintenance, and support across multiple sportsbook-specific business areas, leveraging Google Cloud technologies and advanced AI methodologies.
W hat you'll be getting up to:  Model Development and Deployment: Design, build, and deploy machine learning models to address sportsbook-specific needs using Google Cloud and advanced machine learning techniques.
Real-Time Data Processing: Build systems that leverage streaming data platforms (such as Apache Kafka or Google Pub/Sub) for processing high-frequency sports data feeds, including real-time odds and player stats.
Predictive Analytics: Develop time-series models and statistical analyses to forecast game outcomes, player performances, win/loss probabilities, and other relevant sports betting metrics.
NLP for Customer Engagement: Create sentiment analysis models and personalization algorithms to enhance customer interactions and recommendations based on user behavior and preferences.
Risk Management and Fraud Detection: Implement anomaly detection models to flag potential fraud, detect irregular betting patterns, and manage associated risks, ensuring compliance with industry regulations.
Customer Retention Analytics: Build customer churn prediction models to identify at-risk customers, optimize retention strategies, and improve customer lifetime value (CLV).
A/B Testing and Model Validation: Set up A/B testing frameworks for model validation, evaluating impact on customer engagement and sportsbook profitability.
Data Science Prototype Transformation: Research and refine data science prototypes, transforming them into robust production applications tailored to sportsbook business needs.
Continuous Improvement: Keep up-to-date with the latest advancements in ML and AI, regularly retraining models and enhancing libraries and frameworks.
Incident Response: Provide prompt troubleshooting and incident response, identifying causes and resolving issues as they arise.
Agile Collaboration: Actively contribute to an agile development environment, participate in sprint planning, and share innovative ideas to improve platform functionality.
R equirements: Programming & Frameworks: Strong proficiency in Python (3.8+), with experience in FAST API/Flask for development.
Machine Learning Algorithms: Deep understanding of machine learning algorithms, including supervised, unsupervised, and reinforcement learning techniques relevant to the sportsbook industry.
Transformers and NLP Bots: Skilled in implementing and fine-tuning transformer-based models, with experience in developing NLP bots for customer engagement using Retrieval-Augmented Generation (RAG).
Database Management: Expertise in SQL and PostgreSQL, including writing complex queries, functions, and data models optimized for RAG solutions.
Generative AI and LLMs: Experience with Generative AI tools like PyTorch, Hugging Face, and Scikit-learn, especially within sports analytics applications.
Data Engineering: Proficiency in designing data lakes and pipelines using infrastructure-as-code practices, focusing on real-time sports data ingestion.
Statistical Analysis: Solid understanding of statistical concepts and methodologies, such as regression analysis, sampling theory, and hypothesis testing.
Automation & CI/CD: Hands-on experience with CI/CD pipelines using GitLab, GitHub Actions, Jenkins, and Terraform for continuous deployment.
Problem-Solving & Collaboration: Strong analytical skills, a collaborative mindset, and the ability to work independently or within teams to drive results in an agile environment.
Job qualifications: Bachelor's degree in Computer Science, or related field, or equivalent work experience.
3-5 years of relevant ML/AI experience, especially in high-frequency or sportsbook data applications.
Excellent English communication skills, both oral and written.
Self-starter with the ability to manage multiple priorities in a fast-paced, results-oriented environment.
Technical curiosity and a willingness to explore new technologies and innovations in sports betting and AI.
**We warmly invite applications in English.
Diversity & Inclusion at Sportserve At Sportserve, we are deeply committed to fostering a diverse and inclusive workplace.
We believe in building a team that reflects a wide array of backgrounds, skills, and perspectives.
Embracing diversity not only enriches our work culture but also drives innovation and excellence.
We are proud to be an equal opportunity employer, where everyone's contribution is valued and respected.
If you're a passionate about technology and looking to start your career in an international, forward-thinking Sports Betting company, we'd love to hear from you.
Apply now to become part of our exciting journey