Backend Machine Learning Programmer (AI)

TL;DR

Backend Machine Learning Programmer (Python/AWS): Building scalable services and data infrastructure for AI products with an accent on cloud-native systems and large-scale data pipelines. Focus on deploying and serving ML models, creating AI agents for developer productivity, and optimizing vector search.

Location: Hybrid (Kuala Lumpur, Malaysia). On-site presence is expected at the start; a hybrid arrangement (3 days office, 2 days home) can be agreed later. Fully remote is not offered.

Company

hirify.global is a global leader in gaming services, leveraging the latest technologies to create immersive game experiences and push the boundaries of possibility.

What you will do

• Design and build scalable backend services and APIs for machine-learning and AI features.
• Develop and operate large-scale data pipelines for ingesting and processing big datasets.
• Deploy, serve, and scale ML models in production, including inference and vector search.
• Design and operate AWS cloud infrastructure ensuring reliability, security, and cost-efficiency.
• Implement CI/CD pipelines, observability, and automated testing for backend systems.
• Collaborate with ML and frontend engineers to deliver end-to-end features.

Requirements

• Around 4+ years of backend engineering experience, primarily in Python.
• Hands-on experience with AWS cloud services (compute, storage, containers, serverless).
• Experience with big-data processing and serving machine-learning models.
• Solid grounding in APIs, microservices, containerization, and monitoring.
• Required experience with AI coding assistants (e.g., Claude Code, GitHub Copilot, Codex CLI).
• Excellent written and spoken English proficiency.

Nice to have

• Experience with GitLab and Horde (strong plus) or Jenkins and TeamCity.
• Prior experience in game development.

Culture & Benefits

• Opportunity to work at the forefront of gaming and AI innovation.
• Collaborative environment with a global team of industry experts.
• Hybrid work flexibility after an initial on-site period.
• Focus on professional growth, dedication, and pushing technical boundaries.