Machine Learning Engineer – Agentic Focus

🕒 March 25

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High 5 Games

WebsiteLinkedInAll Job Openings

51 - 200 employees

Founded 1995

🎮 Gaming

🎲 Gambling

🤝 B2B

Gaming • Gambling • B2B

High 5 Games is a developer and supplier of casino slot games and related platform services. The company creates and distributes dozens of online and land-based slot titles (over 150), offers progressive jackpots and in-game promotion tools, and provides a casino platform and game-studio solutions for operators and partners. High 5 Games serves regulated casinos and online operators with both B2B integrations and player-facing social/real-money experiences.

📋 Description

• Design, develop, and deploy machine learning models and solutions, leveraging tools such as LangGraph and MLflow for orchestration and lifecycle management. • Collaborate on building and maintaining scalable data and feature pipeline infrastructure for real time and batch processing using tools like BigQuery, BigTable, Dataflow, Composer(Airflow), PubSub, and Cloud Run to support ML model training and inference. • Develop and implement robust strategies for model monitoring and observability to detect model drift, bias, and performance degradation, leveraging tools like Vertex AI Model Monitoring and custom dashboards. • Optimize ML model inference performance to improve latency and cost-efficiency of AI applications. • Ensure the overall reliability, performance, and scalability of the ML models and data infrastructure platform, including proactive identification and resolution of issues related to model performance and data quality. • Troubleshoot and resolve complex issues impacting ML models, data pipelines, and production AI system. • Ensure AI/ML models and workflows meet data governance, security, and compliance requirements, specifically for real-money gaming.

🎯 Requirements

• 1+ years of experience as an ML Engineer, with a focus on developing and deploying machine learning models in production environments. • Strong experience in Google Cloud Platform (GCP), including services relevant to ML and data infrastructure such as BigQuery, Dataflow, Vertex AI, Cloud Run, and Pub/Sub and Composer (Airflow). • Solid grasp of containerization (Docker, Kubernetes) and experience with Kubernetes orchestration platforms like GKE for deploying ML services. • Experience building and deploying scalable data pipelines and machine learning models in production environments. • Understanding of model monitoring, logging, and observability best practices for ML models and applications. • Experience in Python and ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn). • Familiarity with AI orchestration concepts using tools like LangGraph or LangChain is a bonus. • Bonus experience includes working in gaming, real-time fraud detection, or AI personalization systems and Agentic workflows.