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Student land jobs or accelerate their careers within 180 days of completing the program

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Become an AI Professional

Build job-ready skills through hands-on projects, expert mentorship, and real-world applications to accelerate your career in the digital era.

AI Engineer

The AI Engineering Program equips participants with practical skills to build, deploy, and optimize AI solutions using real-world datasets and industry-standard tools, preparing them for real-world AI implementation roles.
Program Detail

AI Engineering Program

By CLC Instructor

08 June 2026

In House

Rp. 7,5 Juta

Cybertrend Learning Center
  • Overview of Artificial Intelligence (AI).
  • AI vs Automation vs Intelligent Automation.
  • Introduction to Workflow Automation
  • AI applications in business processes.
  • Use cases of AI automation.
  • Introduction to orchestration concepts.
  • Overview of no-code and low-code automation.
  • What is n8n?.
  • n8n architecture and workflow concepts.
  • Self-hosted vs cloud deployment.
  • n8n interface overview.
  • Nodes, connections, and executions.
  • Workflow lifecycle in n8n.
  • Creating first automation workflow.
  • Trigger-based automation.
  • Event-driven workflows.
  • Scheduling workflows.
  • Conditional logic and branching.
  • Error handling concepts.
  • Workflow debugging and monitoring.
  • Best practices for automation design.
  • Understanding APIs.
  • REST API basics.
  • HTTP methods (GET, POST, PUT, DELETE).
  • JSON fundamentals.
  • Authentication methods: API Key, Bearer Token, OAuth.
  • API integration using n8n HTTP Request node
  • Workflow automation concepts
  • Integrating LLM APIs
  • Use cases (chatbots, summarization, data extraction)
  • Error handling and retries
  • Marketing automation (AI content generation, personalization, campaign optimization)
  • Sales automation (lead qualification, CRM workflows, proposal generation)
  • Customer support automation (chatbots, ticket routing, auto-responses)
  • Operations automation (document processing, approvals, workflow orchestration with n8n)
  • Data & reporting automation (automated insights, dashboards, natural language queries)
  • Model packaging & API deployment (menggunakan FastAPI / Flask)
  • Containerization & orchestration (Docker & Kubernetes)
  • CI/CD untuk ML (automasi training, testing, dan deployment pipeline)
  • Monitoring & logging model (data drift, performance, observability dengan tools seperti Prometheus atau Grafana)
  • Model versioning & lifecycle management (contoh: MLflow)
  • Scalability & optimization (load balancing, caching, inference optimization)
  • Cloud deployment (Amazon Web Services, Google Cloud Platform, Microsoft Azure)
  • Security & compliance (API security, data privacy, access control)

About Course

The AI Engineering Program equips participants with practical skills to build and deploy AI applications using LLMs, RAG, and modern AI tools. Through hands-on projects and real-world use cases, participants learn to develop, evaluate, and optimize AI systems for real-world implementation.

What you will learn?

  • Understand the fundamentals of Generative AI and Large Language Models (LLMs)
  • Build AI applications that can generate and process human language
  • Implement Retrieval-Augmented Generation (RAG) for real-world use cases
  • Work with embeddings and vector databases for intelligent data retrieval
  • Design data pipelines to support AI and LLM-based systems
  • Design data pipelines to support AI and LLM-based systems
  • Evaluate AI outputs and improve accuracy, relevance, and reliability
  • Detect and reduce hallucinations in AI-generated responses

Requirements

  • No prior experience in AI required
  • Basic understanding of digital tools and logical thinking
  • Familiarity with programming concepts (Python is a plus)
  • Access to a laptop and stable internet connection
  • Commitment to hands-on learning and real-world projects

Material Includes

  • Structured AI curriculum covering LLMs, RAG, and real-world AI system design
  • Hands-on projects and capstone portfolio using real datasets and modern AI tools
  • Mentorship, learning resources, and certification to support your AI learning journey

Platform Covered :

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Benefits Obtained :

  • Unlimited Access to Our Digital Learning
  • Training Evaluation Test
  • Certificate of Completion
  • Internship Program

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