AI Engineer with 1+ years in Machine Learning, NLP, and Computer Vision
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AI Engineer proficient in end-to-end ML systems across NLP, computer vision, time series forecasting, and generative AI. Skilled in PyTorch, TensorFlow, Hugging Face Transformers, and OpenAI API, with expertise in model training, fine-tuning, and deployment via FastAPI, Streamlit, and Docker. Experienced in integrating LLMs (GPT-40-mini, Mistral) and deep learning models (DistilBERT, BiLSTM, LSTM, YOLOv8) into scalable applications.
Telkom University
Bachelor of Informatics
August 1, 2022 – June 30, 2026
DBS Foundation
AI Engineer Cohort
February 1, 2026 – June 1, 2026
India
Dicoding x Accenture
Machine Learning Cohort
August 1, 2025 – January 1, 2026
India
Home Credit Indonesia x Rakamin Academy
Data Scientist (Project-Based Virtual Intern)
August 1, 2025 – September 1, 2025
India
Rakamin Academy
Class Coordinator Data Science
October 1, 2024 – May 1, 2025
India
Bitcoin Price Forecasting with LSTM & Seq2Seq
April 1, 2026 – April 1, 2026
• Overview: Built a multi-step forecasting system comparing Baseline LSTM and Seq2Seq Encoder-Decoder with custom Multi-Head Attention, custom training loops, and weighted horizon loss for 24-step-ahead Bitcoin price prediction using technical features (RSI, MACD, ATR, KAMAO). • Outcome: Achieved improved forecasting accuracy with Seq2Seq outperforming baseline LSTM; fulfilled all Advanced criteria including custom model architecture, custom training loop with early stopping, and weighted horizon loss.
Face Attendance Microservice
November 1, 2025 – January 1, 2026
• Overview: Built a face recognition microservice using SCRFD (detection), ArcFace (recognition), and Milvus vector database on Azure, deployed via Docker with REST API. • Outcome: Delivered a real-time, scalable attendance system with containerized deployment and vector-similarity-based face matching via REST API endpoints.
Sentiment Analysis for Netflix Reviews using DistilBERT
May 1, 2025 – May 1, 2025
• Overview: Built a sentiment classifier using DistilBERT + Hugging Face Transformers (PyTorch) with data augmentation via T5 paraphrasing on Netflix user reviews dataset. • Outcome: Achieved 93.42% test accuracy; deployed the model as an interactive web application via Streamlit.
Loan Risk Prediction with Generative AI Recommendations
April 1, 2025 – April 1, 2025
• Overview: Trained Random Forest and XGBoost on structured customer data and integrated Mistral LLM via Ollama for personalized financial product recommendations based on model predictions. • Outcome: Achieved 96% prediction accuracy; deployed the full pipeline as an interactive Streamlit application combining risk prediction with LLM-generated recommendations.
Membangun Proyek Deep Learning Tingkat Mahir by Dicoding
Dicoding
April 1, 2026 – Present
Belajar Fundamental Deep Learning by Dicoding
Dicoding
March 1, 2026 – Present
Preparation Course for Azure AI Fundamentals by ElevAlte
ElevAlte
May 1, 2025 – Present
Cultural Fit Analysis
The candidate's diverse project portfolio, including personal projects and internships across different domains (finance, attendance, entertainment), indicates adaptability and a broad interest in AI applications. The involvement in educational cohorts and a class coordinator role suggests a collaborative mindset and a willingness to contribute to a learning environment. The focus on end-to-end ML systems and deployment aligns well with a practical, product-oriented culture.
Soft Skills & Operational Fit
The candidate's experience as a Class Coordinator Data Science suggests organizational and communication skills, which are beneficial for team collaboration and project coordination. The project descriptions are clear and outcome-oriented, indicating good communication of technical work. The candidate's involvement in various cohort programs (DBS Foundation, Dicoding x Accenture) suggests a proactive learning attitude and ability to work within structured programs.