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AI Engineer with less than a year in AI, Machine Learning & Data Analytics
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Fresh graduate from Bandung Institute of Technology with a strong interest in AI & Data related roles, and just completed a thesis on Computer Vision Re-Identification. Agile and dedicated professional skilled in Agentic AI, machine learning, data warehousing, and cloud deployment with hands-on experience in tools like TensorFlow, PyTorch, Vector Database, Langchain, Langgraph, RAG, LLM API tools, Scikit-Learn, Big Data Tools like Snowflake & Big Query, Dataiku, SQL, FastAPI, Python, Jupyter, Postman, and ETL & Machine Learning Pipeline tools, and GCP, S3 bucket, and IBM VPC. Thrives in fast-paced environments, emphasizing collaboration, problem-solving, and cutting-edge AI applications.
Bandung Institute of Technology (ITB)
B.Sc · Information System and Technology
August 1, 2021 – July 1, 2025
Mrscraper
AI Engineer (Fulltime)
September 1, 2025 – Present
India
IBM
Client Engineering Intern - AI & Data (Internship)
May 1, 2025 – August 1, 2025
India
PT Telkom Indonesia
Data Scientist Intern - AI & Data (Internship)
January 1, 2025 – June 1, 2025
India
PT Pupuk Indonesia
Data Scientist Intern - AI & Data (Internship)
June 1, 2024 – December 1, 2024
India
RAG Interview System
June 27, 2026 – Present
Developed RAG Interview System using Llama 3.1 on GCP GPU (V100) with ChromaDB, Ollama Embeddings, NLTK, Tesseract OCR, FastAPI, TTS, and STT, enabling interactive interview simulation with automated CV scanning.
Sentiment analysis for Instagram & Twitter
June 27, 2026 – Present
Implemented sentiment analysis for Instagram & Twitter using Transformer-based models (BERT/ROBERTa) with FastAPI endpoints for real-time API integration.
Multi-class classification model to predict market response to discount strategies
June 27, 2026 – Present
Created multi-class classification model to predict market response to discount strategies using Scikit-learn (Random Forest, XGBoost, Stacking Classifier) with Pandas, NumPy, and Seaborn for exploratory analysis and visualization.
Web scraping API with Requests & Deploy at IBM VPC
June 27, 2026 – Present
Built web scraping API with Requests & Deploy at IBM VPC, BeautifulSoup, FastAPI, StreamSets to extract ~5 topics, ETL into DB2, and visualized insights on IBM Cognos.
Anomaly Detection & Remaining Useful Life (RUL) models
June 27, 2026 – Present
Designed and deployed Anomaly Detection & Remaining Useful Life (RUL) models using Unsupervised ML (Isolation Forest, Autoencoder) and Forecasting (ARIMA, Prophet, Stacking Ensemble), integrated with Snowflake, GCP, Dataiku, Airbyte, FastAPI for scalable data pipelines.
ML Specialization (Top 10%)
Bangkit
June 1, 2026 – Present
Mathematics for ML and Data Science Specialization
Coursera
June 1, 2026 – Present
TensorFlow: Data and Deployment Specialization
DeepLearning.AI
June 1, 2026 – Present
TensorFlow Developer Professional Certificate
DeepLearning.AI
June 1, 2026 – Present
AWS Cloud Operations Training Session
Unknown
June 1, 2026 – Present
Crash Course on Python
Coursera
June 1, 2026 – Present
Architecting on AWS training
Unknown
June 1, 2026 – Present
Google Data Analytics
Coursera
June 1, 2026 – Present
Machine Learning Specialization
Coursera
June 1, 2026 – Present
Cultural Fit Analysis
The candidate exhibits a strong cultural fit for an AI Engineer role, particularly in an innovative and fast-paced environment. Their diverse project portfolio, ranging from generative AI systems to anomaly detection and web scraping, showcases a broad interest and adaptability. The involvement in multiple internships and a full-time role, coupled with academic achievements and certifications, indicates a proactive and continuous learning mindset. The candidate's experience with various cloud platforms and data tools suggests a willingness to work with diverse technology stacks, which is beneficial for team collaboration and project flexibility.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills, adaptability, and a collaborative mindset, as evidenced by their involvement in multiple projects and leadership in a capstone team. Their experience in fast-paced environments and emphasis on collaboration aligns well with dynamic operational settings. The candidate's ability to synthesize research and implement complex data pipelines suggests a methodical and thorough approach to problem-solving.