Junior Machine Learning Engineer – Agentic Focus

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November 9

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High 5 Games

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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.

51 - 200 employees

Founded 1995

🎮 Gaming

🎲 Gambling

🤝 B2B

📋 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 issues impacting ML models, data pipelines, and production AI systems. • Ensure AI/ML models and workflows meet data governance, security, and compliance requirements, specifically for real-money gaming.

🎯 Requirements

• Junior ML Engineer, with a desire to develop and deploy machine learning models in production environments. • Understanding of 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). • Knowledge containers (Docker) for deploying ML services. • Understanding of data pipelines and machine learning models in production environments. • Understanding of model monitoring, logging, and observability best practices for ML models and applications. • Knowledge of 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.