SQRD.tech

Senior Python Engineer (AI-driven Recommendation Platform, iGaming domain)

About the Product

We are developing an AI-powered performance optimization platform for online casinos and sportsbooks. The solution helps operators improve key business metrics by increasing player engagement and loyalty through personalized recommendations and predictive analytics.

The platform collects and processes player behavior data across multiple systems, builds detailed user profiles, and generates actionable insights, including:

• identifying players who misuse bonus programs without long-term retention
• detecting patterns of risky or excessive gaming to support responsible play
• recommending relevant games and activities based on user preferences and behavioral history

You will join a team building scalable recommendation systems, working with large volumes of data, and implementing ML-driven personalization to enhance user experience within iGaming products.

Requirements

• 5+ years of backend development experience with Python
• Strong foundation in mathematics, statistics, or computer science
• Hands-on experience with FastAPI, Django, or Flask
• Solid knowledge of data structures, algorithms, and distributed systems
• Experience with SQL and NoSQL databases (PostgreSQL, Redis, DynamoDB, MongoDB, etc.)
• Practical experience with Kafka or RabbitMQ and asynchronous architectures
• Cloud experience with AWS (Lambda, S3, RDS, SQS, etc.)
• Experience designing and maintaining high-load, scalable systems
• English level — B2 or higher

Nice to Have

• Experience with recommendation systems, data analysis, or ML-based personalization
• Understanding of data pipelines and ETL processes
• Experience with Kubernetes, Docker, and CI/CD pipelines
• Familiarity with Elasticsearch and monitoring/visualization tools (Grafana, Kibana)

Responsibilities

• Design and develop scalable backend services for AI-powered recommendation systems
• Collaborate with data scientists and ML engineers to deploy models into production
• Optimize data storage, processing, and retrieval for large-scale user datasets
• Ensure system reliability, performance, and scalability
• Take part in architectural discussions, code reviews, and mentoring
• Contribute to new features aimed at increasing player engagement and retention

Tech Stack

Python, FastAPI, Django, Flask, Kafka, RabbitMQ, PostgreSQL, Redis, DynamoDB, AWS (Lambda, S3, RDS, API Gateway, CloudWatch), Kubernetes, Pandas, NumPy, SQLAlchemy, Celery, Elasticsearch, Prometheus, Grafana.

We are looking for Data Scientist to join a Data Team working on innovative AI-driven products. 
 

These roles focus on building AI-powered solutions that solve real business challenges and deliver personalized customer experiences. The team leverages Generative and predictive AI to enhance customer journeys and automate operational processes.

Responsibilities:

• Collaborate closely with Product Owners, Data Analysts, Data Engineers, and ML Engineers to design, develop, deploy, and monitor scalable AI and Generative AI solutions
• Drive the adoption of Generative AI across the organization by building solutions using LLMs, embeddings, and RAG architectures
• Work directly with business stakeholders to translate requirements into production-ready AI systems
• Monitor and optimize model and system performance, applying best practices in Data Science, MLOps, and LLMOps
• Continuously improve prompts, models, and pipelines to maximize business impact
• Stay up to date with advancements in LLMs, RAG frameworks, and GenAI technologies to drive innovation

Requirements:
Must-have:

• 5 years of experience in Data Science or Applied Machine Learning
• Strong proficiency in Python and SQL
• Experience building and deploying machine learning models
• Hands-on experience with Generative AI, including:

• Embeddings
• Vector search
• RAG-style architectures
• NLP or transformer-based models
  • Understanding of advanced model evaluation methods (ranking, relevance, retrieval quality, etc.)

Nice to have:

• Experience with Snowflake
• Knowledge of AWS cloud services
• Experience with LLMOps (prompt and version management, monitoring, evaluation frameworks)

About the Company

We are a lead generation company operating in the iGaming industry since 2012. Our mission is to deliver high-quality data and trustworthy information to players through comprehensive one-stop portals, enhancing their betting experience.

Our team consists of industry experts who make this possible. We are dedicated to creating a positive and engaging work environment that promotes growth, collaboration, and high performance.

About the Role

We are seeking a highly experienced Senior Web Developer with strong expertise in Laravel, PHP, WordPress, and database management.

The primary focus of this role is backend development, including building and maintaining scalable web applications, RESTful APIs, and custom WordPress solutions with Gutenberg block development. A solid understanding of database optimization, complex queries, and migrations is essential to ensure efficient data processing.

Key Responsibilities

• Design, develop, and maintain backend systems using Laravel and PHP
• Build custom WordPress solutions with a focus on backend logic and Gutenberg block development
• Develop and integrate APIs for communication between internal systems and third-party services
• Optimize database queries and manage migrations (MySQL, PostgreSQL) to ensure efficient data handling
• Ensure high performance and scalability of backend services
• Troubleshoot and resolve complex backend issues while ensuring smooth front-end integration
• Collaborate closely with front-end developers and DevOps teams to deliver high-quality solutions
• Maintain strong code quality standards through code reviews, unit testing, and adherence to best practices

Requirements

• 8+ years of backend development experience using Laravel and PHP
• Strong experience in WordPress backend development, including custom plugins and Gutenberg block creation
• Experience with ReactJS (particularly for Gutenberg block development)
• Solid expertise in designing and integrating RESTful APIs
• Strong knowledge of database management, including schema design, migrations, and query optimization (MySQL, PostgreSQL)
• Proficiency with version control systems such as Git (GitHub, Bitbucket)
• Good understanding of backend best practices related to security, performance, and scalability
• Experience with server environments, deployment, and maintenance
• Ability to work effectively in an Agile/Scrum environment and manage multiple tasks simultaneously
• Strong analytical skills, attention to detail, and a proactive mindset
• Experience working with CI/CD pipelines and automation processes

Nice to Have

• Basic knowledge of frontend technologies (HTML, CSS, JavaScript) to enhance collaboration with front-end teams
• Experience with Docker or other containerization tools
• Familiarity with cloud platforms such as AWS, Azure, or Google Cloud
• Previous experience in the iGaming industry, especially within sports betting

Domain: iGaming / Gambling
Format: Product company
Experience: 5+ years

About the Company

We are an international product company operating in the gambling sector. Our platform delivers real-time personalization for casino and sportsbook products using advanced machine learning. The solution processes live behavioral, transactional, and contextual data to improve player engagement, retention, and overall performance for operators worldwide.

Our focus is on building production-grade ML systems that directly influence what users see in real time — from game recommendations to personalized content and promotions.

Role Overview

We are looking for a Senior Data Scientist to join a product-focused team working on real-time personalization and recommendation systems for iGaming platforms.

This is a hands-on role that combines modeling, experimentation, and close collaboration with engineering and product teams in a high-load, real-time production environment.

Main Responsibilities

• Develop ML-driven features for casino products using supervised learning (regression, ranking, classification)
• Maintain and improve existing recommendation systems in production
• Enhance models using gradient boosting and other supervised approaches
• Perform data cleaning, preprocessing, and feature engineering
• Design and maintain pre- and post-processing workflows
• Optimize training and inference pipelines for performance and reliability
• Integrate ML models into Airflow pipelines in a multi-tenant environment
• Adapt and configure the solution for different clients (tenants)
• Collaborate closely with product and engineering teams on experimentation and feature delivery

As Part of the Team You Will

• Work cross-functionally with data scientists, engineers, product owners, designers, and researchers
• Analyze large-scale datasets to extract insights for product and business decisions
• Propose, implement, and evaluate ML approaches to solve real business problems
• Support and evolve a recommendation solution used across multiple tenants
• Influence product strategy through research, experimentation, and data-driven insights into user behavior

Experience & Education

• 5+ years of professional experience in data science
• Degree in a quantitative field (Mathematics, Statistics, Computer Science, or similar)

Core Skills

• Strong proficiency in Python and SQL
• Hands-on experience with data processing tools (Pandas, Polars)
• Solid engineering skills for building and maintaining scalable ML systems
• Experience implementing observability in ML pipelines (metrics, logging, alerting)
• Knowledge of Docker and Kubernetes
• Strong analytical mindset with the ability to solve loosely defined problems
• Hands-on experience with supervised ML techniques, including:
  • Regression and ranking models (XGBoost, LightGBM, CatBoost, neural networks)
  • Feature engineering and model evaluation (AUC, NDCG, MSE, uplift metrics)
  • Personalization or recommendation systems
• Proven experience deploying ML models to production (near real-time or batch)
• Solid understanding of statistical methods (A/B testing, significance testing)

Nice to Have

• Production experience with large-scale recommendation systems
• Experience with Airflow, Valkey/Redis, FastAPI in production
• Familiarity with contextual bandits or reinforcement learning
• Experience with AutoML tools