Mlops Backend Engineer

7 days ago

Singapore HPC AI TECHNOLOGY PTE. LTD. Full time

**Responsibilities**:

- Design, develop, and optimize backend systems for MLOps workflows on our compute cloud platform.
- Build and maintain scalable, secure, and high-performance API services to support ML model training, deployment, and monitoring.
- Develop and integrate orchestration systems for managing AI workloads and containerized environments.
- Implement automated model lifecycle management, including versioning, monitoring, and retraining workflows.
- Collaborate with DevOps and infrastructure teams to ensure seamless deployment, scalability, and reliability of cloud-based AI workloads.
- Enhance logging, monitoring, and alerting systems for ML infrastructure performance tracking.
- Optimize resource allocation and scheduling strategies for distributed AI training and inference tasks.
- Work with AI researchers and engineers to streamline ML model deployment and inference pipelines.

**Requirements**:

- Bachelor's or higher degree in Computer Science, Software Engineering, or a related field.
- Strong programming skills in Python, with experience in backend development.
- Experience with cloud computing platforms (e.g., Kubernetes, Docker, AWS, GCP, or Azure).
- Proficiency in MLOps tools and frameworks (e.g., MLflow, Kubeflow, Airflow, Ray, or Argo Workflows).
- Solid understanding of distributed systems, microservices architecture, and API design.
- Hands-on experience with database technologies (SQL and NoSQL).
- Knowledge of infrastructure-as-code (e.g., Terraform, Ansible) and CI/CD pipelines.
- Familiarity with GPU acceleration frameworks (CUDA, NCCL) is a plus.
- Strong problem-solving skills and the ability to work in a fast-paced, collaborative environment.

**Preferred Qualifications**:

- Experience in AI/ML model deployment and serving (e.g., TensorFlow Serving, TorchServe, Triton Inference Server).
- Background in optimizing resource utilization for large-scale AI workloads.
- Knowledge of network and security best practices for cloud-based AI systems.
- Contributions to open-source MLOps projects are a plus.

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