About Me

As a Machine Learning Engineer with five years of experience, I specialize in building and deploying complete, end-to-end ML solutions in production cloud environments. My passion lies in translating complex enterprise problems into impactful, data-driven applications. I focus on developing production-grade Natural Language Processing (NLP) and Decision Support systems.

A core part of my work involves not just building the model, but also the surrounding infrastructure. I have hands-on experience in microservice architecture, using tools like Java and Spring Boot, to build scalable, robust backends for ML applications. I am also deeply involved in the MLOps lifecycle, defining best practices for model monitoring, versioning, and CI/CD integration to ensure that models perform reliably in production.

Key Projects

Decision Support System (Root Cause Analysis)

I engineered and deployed a primary Decision Support system designed for Root Cause Analysis (RCA). This system uses data analysis to automatically diagnose fluctuations in key business metrics and provide predictive insights.

Query-to-Graph Chatbot (NLP)

I designed and built the core semantic parsing and Natural Language Processing (NLP) capabilities for a Query-to-Graph chatbot. This system allows users to ask complex questions in natural language and get answers from structured data. I later enhanced this distributed system with multilingual support and performance optimizations to improve its scalability.

Custom Intent Detection Microservice

I built and maintained a scalable, custom intent detection microservice, creating the backend infrastructure for users to train, deploy, and manage their own ML models within the chatbot ecosystem.

Other Professional Experience

• Conversational AI: I developed and deployed a production-grade model for handling conversational AI (“small talk”), integrating it into the existing chatbot framework to improve user engagement.
• MLOps Platforming: I contributed key research and proof-of-concepts for the design of a company-wide MLOps platform, helping to define best practices for model performance tracking and CI/CD integration.