Deep Dives into AI/ML Systems

Designed an AI-powered knowledge system that converts large volumes of unstructured documents into a searchable, intelligent interface using embeddings and large language models. The system enables semantic retrieval and context-aware responses rather than simple keyword matching.

Build a scalable AI architecture that allows users to query complex document collections using natural language while maintaining response relevance, latency control, and modular deployment.

• Document ingestion pipeline for preprocessing and chunking text
• Embedding generation layer to convert content into vector representations
• Vector database for semantic retrieval
• LLM inference layer to generate context-aware answers
• API layer to integrate retrieval + generation into an intelligent assistant

• Implemented embedding-based semantic search
• Designed modular retrieval + generation pipeline
• Optimized prompt structure for context retention
• Reduced hallucination by grounding responses in retrieved data
• Structured scalable inference architecture

Python for backend orchestration, LLM APIs for generative responses, vector databases for semantic search, NLP preprocessing pipelines, and API services for inference integration.

Engineered a reusable ML pipeline framework that transforms raw datasets into deployable models through structured feature engineering, training, evaluation, and inference service integration.

Bridge the gap between ML experimentation and deployable systems by designing modular pipelines that support reproducibility, scalability, and production-ready model serving.

• Data preprocessing & feature engineering layer
• Model training & evaluation module
• Experiment tracking workflow
• Model packaging and serialization
• API-based inference service for deployment

• Built reusable feature engineering workflows
• Structured consistent model training pipelines
• Implemented evaluation metrics and validation workflows
• Designed API inference architecture
• Reduced deployment friction for ML models

Python for ML workflows, Scikit-learn and Pandas for model development, FastAPI for inference APIs, and containerized deployment structure.