Deskripsi Pekerjaan
Are you a visionary engineer passionate about building the infrastructure that powers next-generation AI? We are looking for an AI Platform Engineer to join our dynamic team. In this role, you will play a critical part in designing, building, and scaling the machine learning platforms that drive our product innovation.
You will work at the intersection of data engineering, MLOps, and software development, ensuring our AI models are performant, reliable, and production-ready. If you have a passion for architecting scalable cloud solutions and a deep interest in artificial intelligence, we want to hear from you.
This is a remote-friendly opportunity based in the Marikina City area, offering the perfect environment for a high-impact engineer to thrive while working with cutting-edge technologies. You will collaborate with cross-functional teams to streamline the model lifecycle and accelerate our deployment velocity.
Tanggung Jawab
- Design, develop, and maintain high-performance AI infrastructure and MLOps pipelines.
- Implement scalable CI/CD workflows for machine learning models to ensure seamless deployment.
- Optimize model training and inference performance across cloud-based environments.
- Collaborate with Data Scientists to transition prototypes into robust, production-ready services.
- Monitor system performance and troubleshoot bottlenecks in large-scale data processing pipelines.
- Establish best practices for versioning, monitoring, and auditing AI models in production.
- Work on infrastructure automation using Infrastructure as Code (IaC) principles.
Kualifikasi
- Bachelor’s degree in Computer Science, Software Engineering, or a related technical field.
- 3+ years of experience in platform engineering, DevOps, or MLOps.
- Strong proficiency in Python, Go, or Java for building scalable backend services.
- Hands-on experience with cloud platforms (AWS, GCP, or Azure) and container orchestration (Kubernetes, Docker).
- Familiarity with ML frameworks such as TensorFlow, PyTorch, or Scikit-learn.
- Solid understanding of distributed systems, databases (SQL/NoSQL), and data streaming technologies.
- Proven ability to bridge the gap between machine learning research and software production.