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CODE.ID Linkedin · Posted 29d ago

AI Engineer

Jakarta

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Indexed description

  • Gathering, cleaning, and labeling large datasets so machine learning models can learn from them.
  • Designing, training, and fine-tuning AI models (like neural networks) from scratch.
  • Converting machine learning models into APIs or software so they can be seamlessly used in apps or websites.
  • Connecting AI models with existing back-end/front-end systems and cloud servers.
  • Evaluating the performance, speed, and accuracy of AI systems, and tweaking them to reduce errors.
  • Monitoring deployed AI systems in production to ensure they adapt to new data and changing business needs

Minimum Qualifications

  • 3+ years of hands-on experience in developing and deploying ML models into real-world business applications or research environments.
  • Strong understanding of ML/DL frameworks such as Jupyter Notebook, Anaconda, TensorFlow, Keras, Scikit-learn, PyTorch, and MXNet.
  • Proven experience working with cloud service platforms (AWS, Azure, or GCP) for ML/DL pipeline orchestration including GPU-based training (CUDA), model evaluation, and deployment (e.g., SageMaker, Docker, or Vertex AI).
  • Proficiency in Python and core data/ML libraries such as Pandas, NumPy, and Scikit-learn.
  • Solid grasp of machine learning algorithms (classification, regression, clustering, feature selection, hyperparameter tuning, etc.).
  • Experience developing and fine-tuning Large Language Models (LLMs) for text, code, or image generation.
  • Understanding of Retrieval-Augmented Generation (RAG) architecture, including knowledge of vector databases (e.g., FAISS, ChromaDB, Milvus) and embedding models.
  • Experience integrating LLMs with external tools, APIs, and data sources through frameworks such as LangChain, LlamaIndex, or similar orchestration layers.
  • Familiarity with MCP (Model Context Protocol) or modern context-sharing protocols for building scalable, composable AI systems.
  • Experience implementing modern AI pipelines involving fine-tuning, prompt engineering, context retrieval, and model evaluation workflows.
  • Knowledge of model optimization and monitoring (latency, throughput, token efficiency, and hallucination detection).
  • Ability to collaborate with cross-functional teams (data engineers, analysts, and software developers) to deliver AI-powered features and applications.
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