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

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