AI Solutions & Platform Lead

PT Asix Indonesia Cerdas; Jakarta Pusat; On-site; IDR 250,000,000 - 500,000,000; Full time

Job summary

PT Asix Indonesia Cerdas is seeking an experienced AI leader to build and govern advanced AI platforms and production use cases. You will drive assessment, design, and productionization of AI/GenAI solutions to meet business needs and scale across the organization.

You will establish scalable platform capabilities, lifecycle processes, monitoring, APIs, and integration with enterprise apps, while ensuring security, privacy, and governance.

Qualifications

• Minimum 10 years of experience in AI, machine learning, data, software engineering, or technology platforms.
• At least 5 years in a managerial or technical leadership role with production AI solutions or platforms.

Responsibilities

• Lead assessment, design, development, productionization and improvement of AI/GenAI solutions.
• Establish scalable AI platform capabilities covering model lifecycle, orchestration, APIs, deployment, monitoring, and integration.
• Establish operational readiness, platform reliability, and continuous improvement for AI workloads.
• Define AI architecture principles, standards, reusable components, and integration patterns.
• Establish monitoring, reliability, cost, performance, and lifecycle practices for production AI workloads.
• Ensure AI solutions follow security, privacy, governance, and risk requirements with GRC.
• Build and develop the AI team, establish engineering practices, and document knowledge sharing.

Skills

AI/ML solution architecture

GenAI/LLM

machine learning lifecycle

production AI implementation

AI platform

MLOps

model lifecycle

APIs

data pipelines

cloud infrastructure

integration with enterprise apps

technology roadmap

team leadership

Education

Bachelor's degree in Computer Science, Artificial Intelligence, Information Technology, Data Science, Engineering, or a related field

Job description

Job Description
Build and lead AI solutions and platform capability, including the transition, operation, and continuous enhancement of AI platforms and production use cases, while establishing scalable and governed AI delivery practices.

Key Responsibilities

AI Solutions: Lead the assessment, design, development, productionization, and continuous improvement of AI and GenAI solutions based on business needs.

AI Platform & Orchestration: Establish and operate scalable AI platform capabilities covering model lifecycle, orchestration, APIs, deployment, monitoring, and integration.

AI Solutions & Platform Transition: Establish operational readiness, platform reliability, and continuous improvement practices for AI solutions and production workloads.

AI Architecture & Standards: Define AI architecture principles, technical standards, reusable components, and integration patterns in coordination with Technology Strategy & Governance.

AI Operations & Performance: Establish monitoring, reliability, cost, performance, and lifecycle practices for production AI workloads and platforms.

AI Governance & Risk: Ensure AI solutions follow applicable security, privacy, governance, and risk requirements in coordination with GRC and relevant technology functions.

People & Capability Development: Build and develop the AI team, establish engineering practices, and reduce dependency on individual specialists through documentation and knowledge sharing.

Minimum Work Experience: Minimum 10 years of experience in AI, machine learning, data, software engineering, or technology platforms, including at least 5 years in a managerial or technical leadership role with responsibility for production AI solutions or platforms.

Industry: Technology, System Integrator, Digital, AI, Software Development, Cloud, or related industries.

Education Level: Bachelor's degree in Computer Science, Artificial Intelligence, Information Technology, Data Science, Engineering, or a related field.

Skill

AI/ML solution architecture, GenAI/LLM, machine learning lifecycle, and production AI implementation

AI platform, MLOps, model lifecycle, orchestration, APIs, data pipelines, and deployment

Cloud and/or hybrid AI infrastructure, including GPU compute and scalable platform environments

AI solution engineering, use-case assessment, prototyping, productionization, and optimization

AI governance, security, responsible AI, model risk, data privacy, and technology standards

Integration of AI capabilities with enterprise applications, digital platforms, and business processes

Technology roadmap, vendor evaluation, platform selection, and solution cost/performance management

Team leadership, capability development, stakeholder management, and cross-functional collaboration