Tuyển dụng AI ENGINEER – (DevOps Team)

TRIVITA AI is hiring an AI Engineer – DevOps (DevOps Team) to design and operate scalable infrastructure and MLOps pipelines supporting AI model development, training, and deployment in healthcare systems.

General Information

Position: AI Engineer – DevOps
Department: DevOps Team
Reports to: Leader AI Engineer

Job Description

Design, build, and maintain scalable infrastructure to support AI model development, training, and deployment.
Automate end-to-end ML pipelines (data ingestion, model training, evaluation, and deployment) using modern MLOps frameworks.
Collaborate with AI engineers, data scientists, and backend developers to optimize CI/CD workflows for AI services.
Implement monitoring and logging systems for model performance and system reliability.
Manage and secure cloud/on-premises GPU clusters, ensuring high availability and efficient resource utilization.
Develop containerized environments (Docker, Kubernetes) for reproducible model deployment.
Ensure compliance with healthcare data security standards and regulatory frameworks (e.g., HIPAA, FHIR).
Support infrastructure optimization, including storage, network, and GPU utilization analysis.
Research and adopt best practices in MLOps, model versioning, and continuous training strategies.

Job Requirements

Bachelor’s degree in Computer Science, Information Technology, or a related field.
2+ years of experience in DevOps, MLOps, or Cloud Infrastructure (AI/ML-related experience preferred).
Strong knowledge of CI/CD tools (GitHub Actions, Jenkins, GitLab CI, ArgoCD).
Experience with containerization and orchestration (Docker, Kubernetes).
Familiarity with ML workflow tools (MLflow, Kubeflow, Airflow, or similar).
Strong scripting skills in Python and Bash, with experience in automation/configuration tools (Terraform, Ansible).
Understanding of model deployment techniques: REST API, gRPC, model registry, inference serving (Triton, TorchServe, etc.).
Experience with cloud services (AWS, GCP, Azure) and on-premise GPU systems.
Good understanding of networking, monitoring, and system security principles.
Excellent collaboration and documentation skills.
Good English.