AI Platform Engineer

Techconnect; Jakarta Pusat; On-site; IDR 320,000,000 - 680,000,000; Full time

Job summary

Techconnect is seeking an AI Platform Engineer to design, build, and maintain scalable AI infrastructure and tooling across the enterprise. You will enable efficient development, deployment, and monitoring of AI/ML solutions.

With 6–10 years total experience and 3–5+ years in AI platform/MLOps in large-scale environments, you will lead data pipelines, ML deployment, CI/CD, cloud infra, and governance alignment to enterprise standards.

Qualifications

• Bachelor’s degree in Computer Science, Engineering or Data.
• 6–10 years total experience in AI/Platform/MLOps.
• Experience leading end-to-end AI platform builds in large-scale environments.

Responsibilities

• Design, build and maintain scalable AI infrastructure and tooling.
• Develop and manage end-to-end AI platforms (data pipelines, ML deployment, CI/CD, monitoring).
• Ensure reliability, security, and governance alignment with enterprise standards.

Skills

MLOps

CI/CD

Cloud infrastructure

Data pipelines

Education

Bachelor's in CS/Engineering/Data

Tools

Kubernetes

Docker

AWS/Azure/GCP

Job description

• The AI Platform Engineer is responsible for designing, building, and maintaining the scalable infrastructure and tooling that enable efficient development, deployment, and monitoring of AI and machine learning solutions across the enterprise.
• This role develops and manages end-to-end AI platforms, including data pipelines, model training environments, MLOps frameworks, CI/CD automation, containerization, cloud infrastructure, and monitoring systems. It ensures reliability, scalability, security, and performance optimization of AI workloads while aligning with enterprise architecture and governance standards.
• Strategically, the AI Platform Engineer accelerates AI adoption by providing standardized, reusable, and production-ready environments that reduce time-to-market for AI initiatives. The role strengthens operational stability, model lifecycle governance, and cross-team collaboration—ensuring AI capabilities are sustainable, secure, and enterprise-grade
• Experience (YoE): 6–10 years total; 3–5+ years in AI platform / MLOps within large-scale environments
• Education: Bachelor’s in Computer Science / Engineering / Data; cloud or DevOps certifications (AWS/Azure/GCP) preferred
• Project Exposure: Led end-to-end AI platform builds (data pipelines, ML deployment, CI/CD, monitoring) with production-scale impact
• Company Coverage: Experience in large enterprises, tech companies, or consulting with multi-business / regional exposure
• Delivery Scope & Scale: Proven track record supporting multiple AI use cases, high data volumes, and enterprise-grade systems