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I'm an AI Engineer specializing in LLM-based systems orchestration including multi-agent workflows, retrieval-augmented generation, and context engineering. I also work in Analytics Engineering, designing analytical data marts and building dashboards focused on measurable business impact.
When I'm not exploring data analysis tools or following AI trends, I enjoy capturing moments through photography, diving into novels and manga, watching anime and movies, listening to music, or playing in online games like Dota 2 and CS2. These diverse interests help me stay creative and bring fresh perspectives to my work.
Driven by extreme curiosity with the help of LLM to answer my questions about certain things, I am able to learn new concepts quickly and apply them to my work and projects.
Python, SQL, R, MATLAB, TypeScript
NumPy, Pandas, Polars, SciPy, Statsmodels, Regex, Excel, Google Sheets
PostgreSQL, MySQL, MSSQL, BigQuery, DuckDB, Snowflake, Neon, Chroma, Pinecone, FAISS, Qdrant, Supabase
Scikit-learn, TensorFlow, PyTorch, spaCy, Hugging Face, LangChain, Agno, OpenAI, Claude, Gemini, Ollama
FastAPI, Flask, Pydantic, Gradio, Streamlit, Next.js, Netlify, React
Docker, MLflow, Prometheus, Grafana, Langfuse, Apache Airflow, Dagster, dbt
BeautifulSoup, Selenium, Requests
Matplotlib, Seaborn, Tableau, Power BI, Looker Studio, Metabase, Preset
Git, GitHub, Cursor, Claude Code, Antigravity, Google Colab, VS Code, Jupyter Notebook, Postman
Always exploring new technologies and research papers in AI/ML
Love working with diverse teams to build impactful solutions
Passionate about tackling complex challenges with data-driven approaches
Github Activity Tracker
Code is my primary language. While GitHub squares don't show the full complexity of a project, they do show my commitment to shipping something every day and iterating in the open.
GitHub
@mcikalmerdekaPublic repos
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Last year
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With visible activity
Member since
2023
Dec 2023
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Daily public activity over the last 365 days.
Longest streak
306 days
Consecutive visible activity days
Peak month
Jan 2026
275 contributions
Latest push
concept-reminder
Updated Aug 2026
Momentum peaked in Jan 2026, and the latest visible push sits in concept-reminder in Jupyter Notebook, updated Aug 2026. That rhythm was backed by a 306-day streak across 353 active days.
Recent repository activity
A quick read on what has been pushed most recently.
A collection of data science projects showcasing analytics, visualization, and machine learning applications across various industries.
Showing 18 of 18 data projects

End-to-end ML solution with interactive Streamlit dashboard for customer segmentation and personality analysis to boost marketing campaign effectiveness.

My approach to data science focuses on extracting meaningful insights from complex datasets through rigorous analysis and statistical methods. I believe in the power of data to drive informed decision-making while maintaining data integrity and ethical considerations.
Prioritizing actionable insights over complex models, ensuring business value
Building robust pipelines that ensure data accuracy, completeness, and reliability
Creating solutions that deliver measurable business impact and drive growth
Advanced AI and machine learning projects focusing on practical applications, model deployment, and production-ready AI systems.
Showing 20 of 20 AI projects

Conversational AI solution with RAG-enhanced SQL generation using Neon Serverless Postgres for natural language database querying.


Comprehensive AI-powered tool with hybrid context system for cover letter generation, employer Q&A assistance, and cold message outreach throughout your job application process.


Browser-based object detection with YOLO26 supporting images, videos, and real-time webcam inference.

Production-style ML model serving application with DistilBERT sentiment analysis, Gradio UI, and full observability stack using Prometheus and Grafana.

AI-powered application structuring knowledge into graph structures and querying it using natural language.

Interactive tool visualizing the complete RAG process with 3D embeddings and observability.

Production-ready travel planning system with parallel agent execution and full observability.

Educational tool demonstrating information flow into AI agent context windows before inference.

Multi-agent AI simulation of Indonesian House of Representatives (DPR) parliamentary processes.

Complete LLM monitoring stack with interactive Gradio chatbot, Prometheus metrics, and Grafana dashboards for real-time observability.

Interactive AI chatbot featuring Live2D animated characters with eye tracking, emotions, document analysis, and multimodal image understanding.


AI-powered weather application with conversational assistant for natural language weather queries and interactive forecasts.

Hybrid Adaptive RAG system with query-time strategy selection, markdown-first ingestion, and intelligent routing between vector search and SQL tools.

Computer vision application with Gradio web UI that detects Snorlax plush toys in images and live webcam feed using HSV color + blob shape analysis, deployed on Hugging Face Spaces.

RAG knowledge base with metadata-filtering based access control, demonstrating role-based document retrieval across HR, Engineering, Sales, and Finance departments.
My approach to AI engineering focuses on creating production-ready systems that are scalable, maintainable, and ethically responsible. I believe in the power of AI to solve complex problems while maintaining transparency and reliability.
Optimizing models for speed and efficiency without sacrificing accuracy
Building systems that can scale from prototype to production seamlessly
Ensuring AI systems are fair, transparent, and beneficial to society
Choose your favorite game and have some fun! Each game offers a unique experience with scoring and challenges.
Classic 3x3 grid game for two players
Control the snake, eat food, and grow!
Classic hand game against the computer
Get 3 in a row to win. Players take turns marking spaces on a 3x3 grid.
Use arrow keys to control the snake. Eat food to grow and increase your score.
Choose rock, paper, or scissors. First to win 3 rounds wins the game.
Explore my articles on Medium and video tutorials on TikTok covering data science, machine learning, and AI topics.

A practical guide to semantic routing for LLM applications. Learn how to use small embedding models to classify intent, reduce costs, cut latency, and route queries to the right static response or specialized agent before calling an expensive model.

A comprehensive guide to data labelling for machine learning. Learn about label types across NLP and computer vision, quality measurement with inter-annotator agreement, active learning strategies, weak supervision with Snorkel, and how to design label schemas that last.

A comprehensive guide to descriptive and inferential statistics with Python code examples. Covers measures of central tendency, dispersion, the Central Limit Theorem, confidence intervals, hypothesis testing, and real-world A/B testing workflows.

A practical guide comparing Small Language Models and Large Language Models. Learn when to use each, understand the cost differences, and explore three core optimization techniques that make SLMs viable: Quantization, LoRA Fine-Tuning, and Flash Attention.

A practical, engineering-focused comparison of Pandas, Polars, DuckDB, and PySpark. Learn how to choose the right data tool based on execution strategy, memory efficiency, syntax, and scale—from small EDA to terabyte-scale distributed processing.

A practical guide to five reranking techniques that fix the most common failure mode in production RAG systems. Covers cross-encoders, reciprocal rank fusion, Cohere Rerank API, ColBERT, and LLM-as-a-Judge with complete code examples.

A comprehensive comparison of three breakthrough vision-language models for document understanding and text extraction. Learn how GLM-OCR, PaddleOCR-VL-1.5, and DeepSeek-OCR2 are fundamentally changing document AI.

Learn how to deliberately design what information AI models see, when they see it, and in what format. This comprehensive guide covers the four principles of context engineering with a hands-on visualizer demonstrating RAG, memory management, and tool integration for production-ready AI systems.

Discover essential tools for AI engineers covering inference optimization, observability, UI development, agent orchestration, and vector search—moving beyond just prompting APIs to architecting production systems.

A comprehensive guide to building a production-ready travel planner with Agno and Langfuse, covering parallel agents, workflows, and full observability.

A comprehensive guide to getting started with AI Agent with Langchain
Learn the basics of neural networks and how they power modern AI.
Step-by-step tutorial on creating your first machine learning model.
Follow me on Medium for in-depth articles and subscribe to my TikTok channel for video tutorials on data science, machine learning, and AI.
Have a project in mind or just want to chat about data science and AI? I'm always excited to discuss new opportunities and interesting challenges.
Available for Projects
Open to discussing data science and AI engineering opportunities