Alex

This personal project, “Nutrify Your Life,” involved developing an end-to-end Retrieval-Augmented Generation (RAG) system to transform an extensive, science-based blog library (1200+ posts) into an interactive, conversational knowledge source.

• The Challenge & Learning Objective: To master the practicalities of building a sophisticated RAG system capable of making a large, unstructured, domain-specific knowledge base accessible via natural language, and to explore architectures directly applicable to industrial use cases.

• Solution & Key Methodologies: I independently designed, developed, and deployed the “Nutrify Your Life” Q&A chatbot. This involved:

  • Systematic web scraping, parsing, and chunking of blog content.
  • Constructing a knowledge base using sentence embeddings and a LanceDB vector database.
  • Implementing an advanced retrieval pipeline with hybrid search (vector + keyword FTS) and Cross-Encoder reranking, including sentence-window retrieval for richer context.
  • Integrating with fast LLMs (via Groq API, e.g., Llama3) for accurate, context-aware answer synthesis with source citation, guided by careful prompt engineering.
  • Developing a user-friendly Streamlit interface, MongoDB-backed monitoring dashboard, and containerizing the application with Docker for deployment on Streamlit Cloud.
  • Rigorous offline evaluation of retrieval (Hit Rate, MRR) and RAG quality (cosine similarity).

• Demonstrated Capabilities:

  • Successfully built and deployed a fully functional AI application, demonstrating end-to-end expertise in GenAI application development, from data ingestion to LLM integration and UI creation.
  • This project solidified my practical skills in the complete GenAI development lifecycle.

• Relevance for Asset Operators & Technology Providers: The RAG methodology & architecture demonstrated here offers transformative potential for industrial applications, delivering significant efficiency gains and democratizing knowledge access. It is directly applicable for:
  • Technology Providers: Integrating intelligent AI assistants into software to provide instant, context-aware support, answer complex user queries based on product documentation, and reduce support overhead.
  • Asset Operators: Creating powerful internal knowledge bases from technical manuals, maintenance logs, safety procedures, and compliance standards, enabling rapid troubleshooting, improved decision-making for field engineers, and streamlined onboarding for new personnel.

• Key Technologies & Methodologies: Generative AI (RAG), Python, Streamlit, LLM APIs (Groq), Sentence Transformers, Vector Databases (LanceDB), Hybrid Search, Cross-Encoder Reranking, Prompt Engineering, Data Scraping/Processing, MongoDB, Docker, Evaluation Metrics (Hit Rate, MRR).

For a leading SaaS provider of industrial diagnostics, I helped enhance their core AI product by designing and deploying a next-generation anomaly detection and MLOps framework.

• The Strategic Challenge: The company’s mission is to provide real-time, reliable diagnostics for complex industrial assets. To maintain market leadership and scale its impact, there was a critical need to enhance the core diagnostic engine with more advanced, scalable, and interpretable machine learning algorithms.

• My Strategic Solution & Implementation: I spearheaded the algorithmic strategy and was a key driver in establishing a robust MLOps framework for the core product. My contributions included:

  • Designing, benchmarking, and deploying a suite of advanced anomaly detection algorithms (causal and non-causal, e.g., Isolation Forest, PCA-clustering) tailored for complex industrial data.
  • Developing a novel mock-fault injection framework for rigorous pre-deployment validation, ensuring the high reliability required for mission-critical systems.
  • Establishing and benchmarking robust MLOps pipelines for efficient model training, scalable deployment, and continuous performance monitoring.
  • Experimenting with different explainability frameworks to pinpoint anomaly drivers, a key feature for end-user adoption and trust.
  • Improving CI/CD practices and transitioning the algorithmic codebase to a newer Python version.

• Measurable Business Value Delivered:

  • Contributed directly to the core technology that enables the company to identify 2 TWh of energy losses annually across its global fleet of 75+ industrial assets.
  • Significantly improved anomaly detection accuracy and model interpretability, reinforcing the platform’s publicly stated 90% diagnostic accuracy and leading to more reliable, actionable fault warnings for end-users.
  • The robust MLOps processes reduced model deployment times and enhanced platform reliability, enabling faster delivery of value to clients.
  • Strengthened the company’s position as an innovator in the industrial AI space by integrating cutting-edge ML techniques into its core product.

• Key Learnings for Asset Operators & Technology Providers: This project underscored the critical importance of robust MLOps, mock-fault validation, and model explainability for building trustworthy and scalable AI in mission-critical industrial systems.

• Key Technologies & Methodologies: Python, Advanced Anomaly Detection Algorithms (AAKR, Isolation Forest, Clustering, Causal Methods), Strategic MLOps Design (Kubernetes, NATS, GitHub Actions, Seldon), Custom Mock-Fault Generation & Validation, Model Explainability (SHAP), Performance Benchmarking, CI/CD (GitHub Actions).

Led data-driven research within a 16-person team, architecting ETL pipelines & applying advanced modeling to extract insights from complex datasets, resulting in first-author publications & secured new research funding

• X-ray Data Analysis Project: Led the analysis of a unique cosmic structure using advanced techniques for background modeling & signal extraction. Designed a custom Python package for profile fitting (MLE, Bayesian inference) to robustly estimate physical parameters, leading to a first-author publication in a top-tier journal.
• Securing Research Funding: Authored & led a successful proposal to the European Space Agency(ESA) for observing time. Developed a sophisticated ETL pipeline (Python/SQL) to identify the target & created a data-driven justification with detailed signal predictions, securing resources against stiff international competition.
• Cosmic Web Detection Project: Contributed specialized scripts & expert guidance to a landmark study that achieved the first statistical detection of X-ray emission from cosmic web filaments. My contribution was instrumental in validating the analysis & ensuring the robustness of this major scientific discovery.
• Automating Analysis Workflows: Developed a reusable, automated pipeline for the team to standardize the processing of space telescope data, orchestrating complex software environments & significantly improving research efficiency & reproducibility

Pioneered a novel statistical method for analyzing faint signals in massive datasets, resulting in an award-winning, first-author publication & securing multiple competitive research grants.

• Advanced Signal Analysis: Engineered a new analysis method to isolate faint signals of large-scale structures from complex cosmic background data. Developed custom models to accurately subtract instrumental noise, revealing insights inaccessible via standard techniques.
• Diagnostic Tool Development: The resulting method served as a new diagnostic tool that statistically characterized previously unresolvable structures. Its significant potential was validated by outperforming conventional approaches, as detailed in my publications