Machine Learning Engineer
Building intelligent systemsfor real-world impact.
I build production-style AI and machine learning systems, from data pipelines and model training to deployment, monitoring, and evaluation with a strong focus on reliability, interpretability, and real-world impact.
Projects
A selection of systems showcasing production-style machine learning, retrieval-augmented generation, forecasting, anomaly detection, and applied AI engineering.
Enterprise AI Assistant (RAG System)
Enterprise documents are large, unstructured, and prone to hallucinated AI responses.
Designed and implemented a production-style Retrieval-Augmented Generation (RAG) system with document ingestion pipelines, embedding search (FAISS/Chroma), hallucination guardrails, citation enforcement, streaming responses, and evaluation testing.
Exchange Rate Forecasting System
Volatile exchange rates require reliable forecasting and drift monitoring.
Built an end-to-end ML forecasting system with feature pipelines, time-series validation, automated model selection, experiment tracking, and live inference for real-time and batch predictions.
Transaction Anomaly Detection System
Fraud detection with no labeled anomaly data.
Developed an unsupervised anomaly detection system using Isolation Forest and autoencoders with percentile-based thresholding and drift monitoring for behavioral change detection.
Skills
Tools and technologies I use to design, build, deploy, and monitor production-style machine learning systems.
Machine Learning & AI Systems
Designing, training, evaluating, and deploying production-style ML systems.
LLMs & Retrieval-Augmented Generation
Building grounded AI systems with guardrails and vector search.
Backend, Deployment & Monitoring
Supporting ML systems in production-like environments.
About
I’m a Machine Learning Engineer with a background in Computer Science and hands-on experience building production-style AI systems across forecasting, anomaly detection, computer vision, and retrieval-augmented generation (RAG).
My work focuses on the full ML lifecycle which encompasses data pipelines and feature engineering to model evaluation, deployment, monitoring, and guardrails. I care deeply about reliability, interpretability, and building systems that are both technically sound and practically useful.
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Contact
I’m open to machine learning engineering roles and applied AI opportunities. If you’d like to collaborate or discuss a project, feel free to reach out.