Machine Learning Engineer (AI)

TL;DR

Machine Learning Engineer (AI): Designing, building, and optimizing machine learning operations and scaling AI models from research to production with an accent on smooth model deployment, monitoring, and lifecycle management across Google Cloud Platform (GCP) infrastructure. Focus on automating workflows, improving model performance, and ensuring reliability for AI applications.

Location: Remote (Canada)

Company

hirify.global is a company in the gaming industry.

What you will do

• Design, develop, and deploy machine learning models and solutions, leveraging tools such as LangGraph and MLflow for orchestration and lifecycle management.
• Build and maintain scalable data and feature pipeline infrastructure for real-time and batch processing using tools like BigQuery, BigTable, Dataflow, Composer(Airflow), PubSub, and Cloud Run.
• Develop and implement robust strategies for model monitoring and observability to detect model drift, bias, and performance degradation.
• 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.
• Troubleshoot and resolve complex issues impacting ML models, data pipelines, and production AI systems.

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).

Nice to have

• Familiarity with AI orchestration concepts using tools like LangGraph or LangChain.
• Experience includes working in gaming, real-time fraud detection, or AI personalization systems and Agentic workflows.