👨‍💻 Professional Summary Experienced AI & Machine Learning Professional with a strong foundation in data science, prompt engineering, and full-stack AI solution development. Proven expertise in Natural Language Processing (NLP), Generative AI, Semantic Search, and large-scale data systems using both structured and unstructured data. Skilled in designing, deploying, and evaluating RAG (Retrieval-Augmented Generation) systems with hybrid search strategies and LLM integration. 🧠 Core Technical Skills Languages & Frameworks: Python, SQL, PySpark, Pandas, Scikit-learn, NLTK, Word2Vec, CoreNLP, LangChain Databases: PostgreSQL, MongoDB, Qdrant, Azure SQL, NoSQL Visualization & BI Tools: Power BI, Tableau, Seaborn, dc.js AI/ML Platforms: OpenAI (GPT-4, GPT-4o), Azure OpenAI, Hugging Face, Cohere, Vertex AI Model Evaluation: BLEU, ROUGE, RAGAS, CrossEncoder, LLM-as-Judge evaluation MLOps & Data Engineering: ETL pipelines, data modeling, Azure Big Data stack 📌 Project Highlights Developed end-to-end RAG Chatbot: Enabled semantic and hybrid search over book and document content with structured chapter-section-subsection-H2 hierarchy. Incorporated Qdrant and Azure AI Search with re-ranking and fallback LLM logic. AI Model Fine-Tuning from SQL DBs: Designed dataset preparation pipeline to extract schema-aware training data from relational databases and fine-tuned LLMs using NL-SQL pairs. GPT-4o Multimodal App: Built vision-integrated pipeline that accepts book pages with text, tables, and images, generating human-like summaries and answers via GPT-4o. Prompt Engineering for Education: Customized LLM outputs for personalized learning applications by crafting tailored prompts aligned with curriculum and pedagogy. AI Project Leadership: Led teams of AI engineers to deliver AI-driven solutions with a focus on accuracy, ethical AI use, and client satisfaction. 🔬 Domain Applications Human Resources: Built intelligent HR FAQ assistants using semantic and hybrid retrieval with department-aware filtering. EdTech: Personalized learning systems using fine-tuned LLMs, prompt-optimized tutoring agents, and AI-evaluated assessments. Finance & Analytics: Developed dashboards, ML-based forecasting, and anomaly detection models with strong data visualization. 📈 AI Evaluation & Optimization Applied RAGAS and custom evaluation pipelines to measure faithfulness, context relevance, and answer accuracy. Tuned retrieval systems using score normalization, alpha-weighted hybrid scoring, and rerankers like bge-reranker and cross-encoder/ms-marco. 🤝 Team & Collaboration Collaborated with data scientists, subject matter experts, and business stakeholders to ensure AI solutions are relevant, explainable, and maintainable. Conducted training sessions and workshops on AI tools, prompt engineering, and ML practices for teams and clients.…👨‍💻 Professional Summary Experienced AI & Machine Learning Professional with a strong foundation in data science, prompt engineering, and full-stack AI solution development. Proven expertise in Natural Language Processing (NLP), Generative AI, Semantic Search, and large-scale data systems using both structured and unstructured data. Skilled in designing, deploying, and evaluating RAG (Retrieval-Augmented Generation) systems with hybrid search strategies and LLM integration. 🧠 Core Technical Skills Languages & Frameworks: Python, SQL, PySpark, Pandas, Scikit-learn, NLTK, Word2Vec, CoreNLP, LangChain Databases: PostgreSQL, MongoDB, Qdrant, Azure SQL, NoSQL Visualization & BI Tools: Power BI, Tableau, Seaborn, dc.js AI/ML Platforms: OpenAI (GPT-4, GPT-4o), Azure OpenAI, Hugging Face, Cohere, Vertex AI Model Evaluation: BLEU, ROUGE, RAGAS, CrossEncoder, LLM-as-Judge evaluation MLOps & Data Engineering: ETL pipelines, data modeling, Azure Big Data stack 📌 Project Highlights Developed end-to-end RAG Chatbot: Enabled semantic and hybrid search over book and document content with structured chapter-section-subsection-H2 hierarchy. Incorporated Qdrant and Azure AI Search with re-ranking and fallback LLM logic. AI Model Fine-Tuning from SQL DBs: Designed dataset preparation pipeline to extract schema-aware training data from relational databases and fine-tuned LLMs using NL-SQL pairs. GPT-4o Multimodal App: Built vision-integrated pipeline that accepts book pages with text, tables, and images, generating human-like summaries and answers via GPT-4o. Prompt Engineering for Education: Customized LLM outputs for personalized learning applications by crafting tailored prompts aligned with curriculum and pedagogy. AI Project Leadership: Led teams of AI engineers to deliver AI-driven solutions with a focus on accuracy, ethical AI use, and client satisfaction. 🔬 Domain Applications Human Resources: Built intelligent HR FAQ assistants using semantic and hybrid retrieval with department-aware filtering. EdTech: Personalized learning systems using fine-tuned LLMs, prompt-optimized tutoring agents, and AI-evaluated assessments. Finance & Analytics: Developed dashboards, ML-based forecasting, and anomaly detection models with strong data visualization. 📈 AI Evaluation & Optimization Applied RAGAS and custom evaluation pipelines to measure faithfulness, context relevance, and answer accuracy. Tuned retrieval systems using score normalization, alpha-weighted hybrid scoring, and rerankers like bge-reranker and cross-encoder/ms-marco. 🤝 Team & Collaboration Collaborated with data scientists, subject matter experts, and business stakeholders to ensure AI solutions are relevant, explainable, and maintainable. Conducted training sessions and workshops on AI tools, prompt engineering, and ML practices for teams and clients.WWWW…