AI Engineer

Job Summary

We are seeking a talented AI Engineer to join our growing technology team and drive the next generation of AI-powered solutions in the iGaming industry. This role will focus on developing and implementing cutting-edge machine learning models, fine-tuning large language models, and building robust AI infrastructure to enhance player personalization, content generation, and predictive analytics across our platforms.

Key Responsibilities

Machine Learning & Model Development:

• Design, develop, and deploy machine learning models for player behavior prediction, churn analysis, and lifetime value optimization
• Fine-tune large language models (LLMs) for content generation and review analysis
• Implement recommendation systems for personalized casino and bonus suggestions

AI Infrastructure & Vector Databases:

• Build and maintain vector databases for semantic search, content similarity, and recommendation engines
• Implement and optimize embedding models for text, user behavior, and content vectorization
• Design scalable AI pipelines using modern MLOps practices and tools
• Manage model versioning, A/B testing, and performance monitoring systems

LangChain & Conversational AI:

• Develop conversational AI systems using LangChain for customer support and player engagement
• Build RAG (Retrieval-Augmented Generation) systems for intelligent content creation and casino information retrieval
• Implement multi-agent systems for complex iGaming workflows and decision-making processes
• Create chatbots and virtual assistants for enhanced user experience

Data Engineering & Analytics:

• Work with large-scale datasets from multiple iGaming platforms
• Implement real-time data processing pipelines for live player analytics
• Develop ETL processes for model training and inference data preparation
• Collaborate with data scientists to transform business requirements into technical solutions

AI-Powered Features:

• Build personalization engines for content, bonuses, and game recommendations
• Develop sentiment analysis systems for review and feedback processing

Required Qualifications

Technical Skills:

• Programming: Proficiency in Python, with experience in R or Scala as a plus
• Machine Learning: Strong foundation in ML algorithms, deep learning, and statistical modeling
• LLM Fine-tuning: Experience with transformer models, PEFT techniques (LoRA, QLoRA), and model optimization
• Vector Databases: Hands-on experience with Pinecone, Weaviate, Chroma, or similar vector storage solutions
• LangChain: Proficiency in building applications with LangChain, LangGraph, and related frameworks
• Cloud Platforms: Experience with AWS, GCP, or Azure ML services
• Frameworks: PyTorch, TensorFlow, Hugging Face Transformers, scikit-learn
• Databases: SQL, NoSQL, and experience with time-series databases
• MLOps: Docker, Kubernetes, MLflow, Weights & Biases, or similar tools

Experience:

• 3+ years of experience in AI/ML engineering or related roles
• Experience with production ML systems and model deployment
• Background in recommendation systems, NLP, or computer vision
• Previous work with large-scale data processing and real-time systems

Preferred Qualifications:

• Knowledge of responsible AI practices and bias detection
• Experience with multi-modal AI systems (text, image, structured data)
• Familiarity with graph neural networks and knowledge graphs
• Understanding of A/B testing and experimental design

What We Offer

• Competitive Salary: Market-leading compensation package with performance bonuses
• Growth Opportunities: Work at the forefront of AI in the rapidly evolving iGaming industry
• Flexible Work: Remote work model
• Learning & Development: Budget for conferences, courses, and AI/ML certifications
• International Exposure: Collaborate with global iGaming partners and clients
• Health & Wellness: Comprehensive health insurance and wellness programs
• Team Culture: Join a results-oriented team that values professionalism and collaboration

About Our Tech Stack

• AI/ML: PyTorch, Hugging Face, LangChain, OpenAI APIs, Anthropic Claude
• Vector DBs: Pinecone, Weaviate, TypeSense for semantic search and recommendations
• Cloud: Multi-cloud strategy
• Data: Redis, PostgreSQL
• Monitoring: MLflow, Weights & Biases, Grafana, DataDog
• Infrastructure: Docker, Kubernetes, Terraform, GitHub Actions