Machine Learning Engineer – Agentic Focus

📋 Description

• Design, create, and implement machine learning models and solutions, utilizing tools such as LangGraph and MLflow for orchestration and lifecycle management.

• Collaborate on the development and maintenance of 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 facilitate ML model training and inference.

• Develop and execute strong strategies for model monitoring and observability to identify model drift, bias, and performance degradation, employing tools such as Vertex AI Model Monitoring and custom dashboards.

• Enhance ML model inference performance to boost latency and cost-effectiveness of AI applications.

• Ensure the reliability, performance, and scalability of the ML models and data infrastructure platform, proactively identifying and resolving issues related to model performance and data quality.

• Diagnose and address complex issues affecting ML models, data pipelines, and production AI systems.

• Guarantee that AI/ML models and workflows comply with data governance, security, and regulatory requirements, particularly for real-money gaming.

⛳️ Requirements

• A minimum of 1 year of experience as an ML Engineer, concentrating on the development and deployment of machine learning models in production settings.

• Extensive experience with Google Cloud Platform (GCP), including services pertinent to ML and data infrastructure such as BigQuery, Dataflow, Vertex AI, Cloud Run, and Pub/Sub, along with Composer (Airflow).

• A solid understanding of containerization (Docker, Kubernetes) and experience with Kubernetes orchestration platforms like GKE for deploying ML services.

• Proven experience in constructing and deploying scalable data pipelines and machine learning models in production environments.

• Knowledge of model monitoring, logging, and observability best practices for ML models and applications.

• Proficiency in Python and ML frameworks (e.g., TensorFlow, PyTorch, scikit-learn).

• Familiarity with AI orchestration concepts using tools like LangGraph or LangChain is considered a plus.

• Additional experience in gaming, real-time fraud detection, or AI personalization systems and Agentic workflows is a bonus.

🏝️ Benefits

• Competitive salary and performance-based bonuses.

• Opportunities for professional development and continuous learning.

• Flexible work environment with remote working options.

• Health, dental, and vision insurance packages.

• Participation in company-sponsored events and team-building activities.