Senior Software Engineer (AI)

TL;DR

Senior Software Engineer (AI): Building and optimizing high-performance machine learning infrastructure and data pipelines for enterprise marketing solutions with an accent on MLOps, scalability, and system reliability. Focus on architecting robust job execution frameworks using Kubernetes, integrating research models into production, and ensuring observability of complex AI services.

Location: Must be based in Taipei, Taiwan

Company

hirify.global is a SaaS company specializing in AI-driven business decision-making, helping organizations turn data into ROI through intelligent software.

What you will do

• Design and operate robust ML job execution frameworks for training and inference.
• Maintain internal API servers and orchestrate ML workflows using Kubernetes, Argo, and Helm.
• Architect and scale batch data pipelines using Spark for model training.
• Ensure system observability and high availability via Prometheus and Grafana.
• Develop internal tools to simplify ML experimentation and production cycles.
• Collaborate with ML scientists to transform research outputs into high-quality product features.

Requirements

• Bachelor’s degree in Computer Science or a related field.
• Minimum 3+ years of experience in ML platform engineering, MLOps, or data infrastructure.
• Proficiency in Python, Java, or Go.
• Solid understanding of data structures, algorithms, and distributed computing concepts.
• Must be physically located in Taipei, Taiwan.
• Ability to work effectively in a cross-functional environment.

Nice to have

• Experience in the MarTech domain.
• Deep knowledge of cloud-native ecosystems and tools like MLflow or Kubeflow.
• Strong architectural thinking for leading platform initiatives.
• Practical experience with end-to-end ML production workflows.

Culture & Benefits

• Opportunity to work with cutting-edge AI technologies in a listed company.
• Global exposure with offices spanning APAC, Europe, and the U.S.
• Collaborative environment focused on impact-driven engineering.
• Exposure to large-scale data and enterprise-grade AI challenges.