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AI Engineer with less than a year in Machine Learning & NLP
Aspiring Data Scientist and MCA graduate with hands-on experience in Machine Learning, Natural Language Processing (NLP), Large Language Models (LLMs), Statistical Analysis, and Data Visualization. Proficient in Python, SQL, Power BI, Pandas, NumPy, Scikit-learn, TensorFlow, and PyTorch. Experienced in developing AI-powered applications including Retrieval-Augmented Generation (RAG) chatbots, real-time meeting summarization systems, and HR analytics dashboards. Skilled in data cleaning, exploratory data analysis (EDA), predictive modeling, statistical modeling, hypothesis testing, and business intelligence reporting. Strong analytical and problem-solving abilities with a passion for leveraging data-driven insights and AI technologies to solve complex business challenges
Cultural Fit Analysis
The candidate's project diversity, including RAG chatbots, meeting summarizers, and HR analytics dashboards, indicates adaptability to various problem domains. However, the limited professional experience (internships only) and lack of diverse team or organizational exposure beyond internships might suggest a need for further development in broader cultural integration. The target role of 'AI Engineer' aligns well with the candidate's stated skills and project experience.
Soft Skills & Operational Fit
The resume highlights problem-solving, analytical thinking, communication, and team collaboration. These soft skills are crucial for an AI Engineer role, especially in collaborative development environments. The candidate's experience in developing end-to-end AI applications suggests an operational fit for roles requiring practical implementation.