Employment Opportunity - AI Platform Engineer

Job Summary Support the implementation and management of AI platform infrastructure using container technologies and automation tools to enable scalable, secure, and efficient AI/data platform operations. Responsibilities

• Support implementation of AIDA’s AI Platform on RE:AI’s IaaS using Red Hat OpenShift or equivalent container platforms to ensure stable and scalable deployment

• Integrate core platform components including API Gateway, RAG pipelines, agent orchestration, and observability stacks to enable seamless platform functionality

• Assist in deployment and management of OpenShift clusters (CPU/GPU), including configuring operators, networking, and security settings to maintain cluster health and security

• Automate platform provisioning and operations using Infrastructure-as-Code (IaC), CI/CD pipelines, and standardized deployment patterns to improve deployment efficiency and consistency

• Integrate platform capabilities with enterprise infrastructure such as networking, storage, load balancers, and backup systems to ensure robust platform connectivity and data protection

• Implement platform security controls including IAM/AD, SIEM, NGFW, and EDR aligned with enterprise security standards to safeguard platform integrity

• Support AI/ML workloads including GenAI, LLM serving, RAG pipelines, and agentic frameworks to optimize AI platform performance

• Contribute to platform monitoring, logging, performance tuning, and capacity planning to maintain operational excellence and scalability

Required competencies and certifications

• Bachelor’s degree in Computer Science, Engineering, or a related field

• Minimum 3 years of experience in platform engineering, cloud-native infrastructure, or AI/data platform roles

• Hands-on experience with Kubernetes/OpenShift including operators, networking, and security configuration

• Familiarity with Infrastructure-as-Code (IaC) and DevOps/CI/CD practices

• Basic understanding of cloud/IaaS networking and security principles

Preferred competencies and qualifications

• Exposure to AI/ML platform components such as LLMs, RAG, or similar frameworks

• Red Hat, Kubernetes, or OpenShift certifications