AI/ ML Engineer with hands-on industry experience designing and deploying end-to-end machine learning and LLM-powered systems and intelligent AI agents. Skilled in building scalable data pipelines, feature engineering wo…
AI/ ML Engineer with hands-on industry experience designing and deploying end-to-end machine learning and LLM-powered systems and intelligent AI agents. Skilled in building scalable data pipelines, feature engineering workflows, predictive models, and enterprise-grade Retrieval-Augmented Generation (RAG) architectures with hybrid search and re-ranking. Strong expertise in Python, deep learning frameworks, backend API development, and database integration. Experienced with Docker-based deployment, cloud ML environments, and MLOps practices including experiment tracking, logging, monitoring, and model lifecycle management.
<p>Designed and implemented a multimodal RAG system processing 1,000+ document chunks to answer domain-specific queries from complex PDF documents.<br>
Built Retrieval-Augmented Generation (RAG) pipelines using LangChain, ingestion, document chunking, embedding generation, vector indexing, hybrid search, and re-ranking to improve retrieval accuracy.<br>
Integrated structured databases (PostgreSQL/MySQL) for document metadata management and dynamic query handling.<br>
Engineered advanced RAG reasoning logic to minimize hallucinations and improve contextual answer consistency.<br>
Refined LLM-based SQL query generation using prompt engineering techniques for accurate database retrieval.<br>
Expanded RESTful APIs using FastAPI, enabling real-time query responses for AI-driven applications.<br>
Enhanced structured logging, monitoring, and error-handling mechanisms to improve system reliability and debugging.<br>
Developed machine learning forecasting models to predict product demand using historical sales time-series data.<br>
Conducted exploratory data analysis and identified highly intermittent demand patterns with a large proportion of zero-sales periods.<br>
Performed time-series anomaly detection to identify irregular demand spikes and outliers.<br>
Generated predictive features including lag variables, rolling statistics, and temporal indicators to capture demand trends.<br>
Applied forecasting algorithms including XGBoost, Random Forest, and Croston methods to model intermittent demand behavior.<br>
Constructed a stacking ensemble model combining multiple algorithms to improve prediction stability and robustness.<br>
Assessed forecasting performance using MAE, RMSE, and R2 evaluation metrics.<br>
Implemented recursive forecasting techniques to generate multi-step future demand predictions.</p>
Designed and implemented a multimodal RAG system processing 1,000+ document chunks to answer domain-specific queries from complex PDF documents., Built Retrieval-Augmented Generation (RAG) pipelines using LangChain, ingestion, document chunking, embedding generation, vector indexing, hybrid search, and re-ranking to improve retrieval accuracy., Integrated structured databases (PostgreSQL/MySQL) for document metadata management and dynamic query handling., Engineered advanced RAG reasoning logic to minimize hallucinations and improve contextual answer consistency., Refined LLM-based SQL query generation using prompt engineering techniques for accurate database retrieval., Expanded RESTful APIs using FastAPI, enabling real-time query responses for AI-driven applications., Enhanced structured logging, monitoring, and error-handling mechanisms to improve system reliability and debugging.
<p>• Architected a multimodal Retrieval-Augmented Generation (RAG) system capable of answering domain-specific queries from complex PDF documents containing text and tabular data, processing 1,000+ document chunks.</p><p>• Constructed end-to-end RAG pipelines with LangChain, including document ingestion, chunking, embedding generation, vector indexing, hybrid search, and re-ranking to improve retrieval relevance.</p><p>• Integrated PostgreSQL and MySQL databases to manage document metadata and support scalable query processing.</p><p>• Implemented advanced reasoning logic within the RAG pipeline to reduce hallucinations and improve contextual answer consistency.</p><p>• Optimized LLM-driven SQL query generation through prompt engineering techniques for accurate database retrieval.</p><p>• Developed FastAPI-based REST services enabling real-time responses for AI-powered applications.</p><p>• Established structured logging, monitoring, and error-handling pipelines to enhance system reliability and simplify debugging.</p>
<p>• Designed machine learning models to forecast product demand from historical sales time-series data.</p><p>• Conducted exploratory data analysis identifying highly intermittent demand patterns with a significant proportion of zero-sales periods.</p><p>• Performed time-series anomaly detection to identify irregular demand spikes and anomalies.</p><p>• Generated predictive features including lag variables, rolling statistics, and seasonal indicators to capture temporal patterns.</p><p>• Applied forecasting algorithms including XGBoost, Random Forest, and Croston methods to model intermittent demand.</p><p>• Constructed a stacking ensemble framework combining multiple algorithms to improve prediction stability and generalization.</p><p>• Assessed model performance using MAE, RMSE, and R² metrics to validate predictive accuracy.</p><p>• Implemented recursive forecasting strategies to generate multi-step future demand predictions.</p>
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