Role overview

AI Platform Engineer

Requirements and responsibilities

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Key Responsibilities

  • Support the implementation and enhancement of an enterprise AI Platform running on cloud-native infrastructure using Red Hat OpenShift or equivalent Kubernetes platforms.
  • Build and integrate AI platform capabilities including API Gateway, Retrieval-Augmented Generation (RAG) pipelines, AI agent orchestration, model serving, and observability components.
  • Deploy, configure, and manage Kubernetes/OpenShift clusters across CPU and GPU environments.
  • Automate infrastructure provisioning and platform operations using Infrastructure-as-Code (IaC), CI/CD pipelines, and DevOps best practices.
  • Integrate AI platform services with enterprise infrastructure including networking, storage, load balancers, backup, and security services.
  • Implement platform security controls including Identity & Access Management (IAM), Active Directory integration, SIEM, Endpoint Detection & Response (EDR), and Next Generation Firewalls (NGFW).
  • Support AI/ML workloads including Large Language Models (LLMs), Generative AI applications, RAG architectures, and agent-based AI frameworks.
  • Monitor platform health, optimize performance, manage logging, and support capacity planning to ensure high availability and scalability.
  • Collaborate closely with AI engineers, data scientists, DevOps engineers, and enterprise technology teams to deliver secure and reliable AI platform capabilities.
  • Continuously evaluate emerging cloud-native and AI platform technologies to improve platform capabilities and operational efficiency.

Key Requirements:

  • Bachelor's Degree in Computer Science, Information Technology, Engineering, or a related discipline.
  • Minimum 3 years of experience in Platform Engineering, Cloud Infrastructure, Kubernetes, DevOps, or AI/Data Platform Engineering.
  • Hands-on experience with Kubernetes/OpenShift, including cluster deployment, networking, operators, and security.
  • Experience with Infrastructure-as-Code (Terraform, Ansible, or similar tools).
  • Familiarity with CI/CD pipelines and DevOps automation practices.
  • Good understanding of enterprise networking, cloud infrastructure, and security principles.
  • Exposure to AI platform technologies including:Large Language Models (LLMs)Retrieval-Augmented Generation (RAG)AI inference/model servingAgentic AI frameworks (preferred)
  • Large Language Models (LLMs)
  • Retrieval-Augmented Generation (RAG)
  • AI inference/model serving
  • Agentic AI frameworks (preferred)
  • Knowledge of monitoring and observability tools for distributed platforms.
  • Strong analytical, troubleshooting, and problem-solving skills.
  • Excellent communication and stakeholder collaboration abilities.
  • Passion for learning emerging AI and cloud-native technologies.

Details

  • Large Language Models (LLMs)
  • Retrieval-Augmented Generation (RAG)
  • AI inference/model serving
  • Agentic AI frameworks (preferred)

Preferred Qualifications

  • Red Hat OpenShift Certification.
  • Certified Kubernetes Administrator (CKA) or Certified Kubernetes Application Developer (CKAD).
  • Experience working with GPU-enabled Kubernetes environments.
  • Exposure to MLOps platforms and AI infrastructure.
  • Experience supporting enterprise-scale AI or cloud transformation initiatives.

Why Join Us

  • Be part of a large-scale AI transformation initiative.
  • Work with cutting-edge AI, Generative AI, and cloud-native technologies.
  • Collaborate with highly skilled engineering and AI teams.
  • Opportunity to shape enterprise AI platforms that power next-generation business capabilities.
  • Continuous learning and professional development in one of the fastest-growing technology domains.
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Browse stack
FocusAI Platform EngineeringRole area
Seniority signalMiddleCandidate level
StackCI/CD, KubernetesPrimary skills
Location1 accepted countryEligibility

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