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AI Engineer with 4+ years in Generative AI, Machine Learning & NLP
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Assessing your cultural and operational fit
AI/ML Engineer with 3+ years of experience designing and deploying enterprise-grade Generative Al, Agentic Al, Machine Learning, NLP and Computer Vision solutions. Hands-on expertise in building production-ready RAG systems, multi-agent workflows, LLM-powered applications, OCR solutions, and predictive analytics models using LangChain, LangGraph, OpenAI APIs, Hugging Face, TensorFlow and PyTorch. Experienced in vector databases, prompt engineering, MLOps pipelines, Azure cloud deployments, Docker containerization and scalable Al architectures. Passionate about leveraging LLMs and autonomous Al agents to solve complex business challenges.
THOUGHTPROCESS SOFTTECH PRIVATE LIMITED
AI Engineer | Generative AI Engineer | LLM & Agentic AI Specialist
May 1, 2023 – Present
India
Ca-One India Tech Cloud
React Developer
July 1, 2022 – February 1, 2023
India
Intelligent RAG-Based Enterprise Knowledge Assistant
June 1, 2026 – Present
Designed and developed a Retrieval-Augmented Generation (RAG) application to improve enterprise document search and contextual question-answering capabilities using LangChain and LLMs. Implemented semantic search pipelines with embedding models and vector similarity search to retrieve highly relevant information from large unstructured datasets. Built efficient document chunking and preprocessing workflows to enhance retrieval accuracy and response quality for enterprise-level knowledge management. Integrated Hugging Face transformer models and prompt engineering techniques to generate accurate and context-aware responses for users. Conducted testing, optimization, and evaluation of retrieval performance to improve scalability, response latency, and overall system efficiency.
Real-Time Number Plate Recognition System
June 1, 2026 – Present
Developed a real-time Automatic Number Plate Recognition (ANPR) system capable of detecting and extracting vehicle registration numbers from surveillance camera feeds. Built image preprocessing pipelines using OpenCV for noise reduction, image enhancement, contour detection, and bounding box generation for accurate plate localization. Implemented OCR-based character recognition workflows to extract alphanumeric information from detected license plates with improved recognition accuracy. Trained and fine-tuned deep learning models on annotated datasets to improve object detection performance under varying lighting and environmental conditions. Performed extensive model validation, testing, and optimization to ensure reliable real-time monitoring and traffic analytics capabilities.
Machine Learning-Based Wine Quality Prediction
June 1, 2026 – Present
Developed a supervised machine learning model to predict wine quality using physicochemical attributes and statistical analysis techniques. Performed data preprocessing, feature engineering, exploratory data analysis (EDA), and outlier handling to improve dataset quality and model performance. Implemented multiple classification algorithms including Random Forest and Logistic Regression, followed by hyperparameter tuning and model evaluation. Achieved an overall prediction accuracy of 88% by optimizing feature selection and improving model generalization techniques. Visualized insights and model evaluation metrics using Matplotlib and Seaborn to support data-driven analysis and decision-making.
Cultural Fit Analysis
The candidate's experience spans Generative AI, Computer Vision, and traditional Machine Learning, demonstrating a broad interest and adaptability in AI domains. The professional experience and projects align well with an AI Engineer role, indicating a clear career focus. The mention of Agile development suggests familiarity with collaborative work environments. However, the lack of diverse project types beyond core AI/ML might indicate a narrower scope of interest, though deep specialization is also valuable for cultural fit in a focused AI role.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on complex, multi-faceted AI systems, suggesting strong problem-solving and technical execution skills. Collaboration with engineering teams for integration into production systems implies good teamwork and operational awareness. The detailed project descriptions also show a structured approach to development, from design to optimization.