AI/ML Engineering
Designing and deploying production-grade AI systems—from RAG pipelines and multi-agent architectures to end-to-end MLOps on multi-cloud infrastructure.
Lead Data Scientist & ML Engineer
Theoretical Physics · Artificial Intelligence · Finance
Currently heading the model.fit() efforts with my team at
Solusi247
I started in theoretical physics—drawn to fundamental questions about how things work. That curiosity led me to artificial intelligence, then to finance, and eventually to leading engineering teams building production AI systems. The thread through all of it is simple: I love geting to know how everything works, building things and solving hard problems with brilliant people.
Today I lead a team of data scientists and ML engineers, designing and deploying AI/ML solutions that turn ambitious ideas into real, recurring revenue. My approach blends technical depth with business pragmatism—I care as much about why we’re building something as how we can build it effectively.
Designing and deploying production-grade AI systems—from RAG pipelines and multi-agent architectures to end-to-end MLOps on multi-cloud infrastructure.
There's no AI/ML model without data science, thorough understanding of the data science work flow from exploratory data analysis all the way to data storytelling.
Academic background in physics, artificial intelligence and finance. Paired with project experience in logistics, telco and government sectors to name a few.
Leading teams of data scientists and ML engineers with a focus on code quality, agile delivery, and aligning technical roadmaps with business strategy.
Translating technical capabilities into business value. Bridging the gap between engineering teams and C-level stakeholders to drive data-informed decisions.
Exploring the frontier of context engineering, agentic AI, and sensor fusion—turning research into practical, deployed solutions that solve real problems.
Led cross-functional team to design, deploy, and maintain scalable AI/ML solutions for IT operations, transforming initial PoC into recurring revenue project (now in third phase). Implemented Kubeflow pipelines, monitoring with Grafana, and established ML model lifecycle management.
Built modular multi-agent framework (LangGraph) deployed on OSINT situation room and weekly newsletter generator for media analytics platform. Demonstrated cross-product reusability of agent architectures.
Developed inertial navigation algorithms using sensor fusion (Extended Kalman Filter) and Zero Velocity Update for real-time drift correction in autonomous systems. Achieved <2m position error over 1km through collaboration with embedded engineering teams.
Developed ML-based predictive maintenance model and vehicle breakdown root cause analysis utilising vehicle telematics and geospatial data on AWS. Identified potential locations of low-quality, unregulated fueling spots across Indonesia.
Applied linear regression, clustering (asset selection), and Monte Carlo methods for portfolio construction. Extracted Alpha, Beta, Sharpe, and Sortino Ratios from stock price time-series data.
Technologies and tools I work with daily
Top graduate of the International MBA class, April 2022 graduation batch.
Swansea University Physics Department — One of two recipients selected from 100+ graduating physics students.