About Bitus Labs Bitus Labs is a cutting-edge AI Gaming company dedicated to revolutionizing the gaming industry through innovative artificial intelligence technologies. As we continue to grow, we are building a robust data platform to support our ambitious projects and empower our AI-driven solutions.
About the Role We are seeking a talented and passionate Machine Learning Engineer to join our growing AI team. In this role, you will bridge the gap between research innovation and production deployment, working closely with Research Scientists to implement, optimize, and deploy machine learning models while building robust MLOps infrastructure.

SKILLS:

• Strong problem-solving abilities and analytical thinking
• Excellent communication skills and ability to explain complex technical concepts
• Detail-oriented with a focus on code quality and system reliability
• Self-motivated with the ability to work independently and as part of a team
• Adaptable to a fast-paced environment and changing requirements

• Model Development and Optimization:
• Collaborate with Research Scientists to translate algorithmic prototypes into production-ready machine learning models
• Implement, train, and fine-tune machine learning models with a focus on reinforcement learning, time series modeling, and recommender systems
• Optimize model performance through parameter tuning, feature engineering, and architecture selection
• Debug complex ML systems and resolve technical roadblocks in the model development pipeline
• Model Deployment and Infrastructure:
• Deploy models as APIs or microservices within our system architecture
• Design and implement scalable ML infrastructure to support model development and deployment
• Configure and optimize hardware acceleration (GPU/TPU) for efficient model training and inference
• Implement distributed training systems to handle large-scale machine learning workloads
• MLOps and Automation:
• Design and maintain end-to-end MLOps pipelines including continuous integration, deployment, and monitoring
• Automate model training, evaluation, and deployment workflows
• Build robust monitoring systems for tracking model performance and detecting drift or degradation
• Develop data validation pipelines to ensure data quality and integrity throughout the ML lifecycle
• Implement version control for models, data, and configurations
• Collaboration and Support:
• Work closely with Data Scientists to implement data processing pipelines and feature engineering
• Partner with backend engineers to integrate ML models into gaming applications
• Support Research Scientists in experimental design and prototype development
• Document ML systems and processes for knowledge sharing across the organization
• Develop internal tools to improve ML development efficiency and reproducibility