I'm a Machine Learning Engineer passionate about designing and deploying intelligent systems at scale.
With 3+ years of experience in finance analytics and applied machine learning, I specialize in:

• Building production ML systems with RAG, LLM integration, and semantic search
• End-to-end ML pipelines from data ingestion to model deployment
• Backend optimization using Flask, FastAPI, and scalable architectures
• AI automation - integrating machine learning into real-world workflows
• Predictive modeling - time series forecasting, classification, and optimization

I believe in writing clean, maintainable code and building systems that actually work in production.
Currently exploring local LLM deployments, advanced retrieval systems, and intelligent automation pipelines.

💡 "Production-ready beats perfect-but-stuck-in-dev" — That's my philosophy.

• Production ML Systems - RAG, semantic search, and intelligent retrieval
• LLM Integration - local & cloud-based LLM deployment and fine-tuning
• API Architecture - designing scalable backend systems with FastAPI & Flask
• Model Deployment - end-to-end ML pipelines with fallback mechanisms
• Data Engineering - ETL pipelines, data quality, and efficient indexing (FAISS, Elasticsearch)
• MLOps & Automation - workflow optimization, batch processing, and intelligent automation

Currently deepening expertise in:

• Large Language Models - fine-tuning, LoRA, prompt engineering, RAG systems
• Semantic Search & Retrieval - FAISS, vector databases, embedding optimization
• Model Optimization - quantization, distillation, inference optimization for edge
• MLOps & Deployment - containerization, model versioning, monitoring & logging
• Advanced Analytics - causal inference, feature engineering, A/B testing frameworks

"Shipping is a feature. Perfect is not."
— My mantra as an ML Engineer

⚡ *"Let's build something intelligent together"* — Open for ML engineering roles & collaborations!