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Data Science with less than a year in AI, ML, and Computer Vision, specializing in RAG systems and M
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Final-year AI & ML student with hands-on experience building Retrieval-Augmented Generation (RAG) systems, end-to-end ML pipelines, and computer vision applications. Built an enterprise-grade multi-department banking assistant using LangChain, ChromaDB, and Groq LLM, alongside a churn prediction REST API (87.4% accuracy) and a multi-model food image classifier. Skilled in Python, PyTorch, XGBoost, LangChain, LangGraph, vector databases, and MLOps tools (MLflow, DVC). Currently applying ML and GenAI skills as an AI/ML intern at Vihara Tech.
Cultural Fit Analysis
The candidate's projects demonstrate a breadth of application areas (banking, food classification, customer churn), indicating adaptability and a willingness to tackle different problem domains. The target role is 'Data Science', which aligns well with the candidate's stated skills and project experience. However, the lack of team-based project descriptions or explicit collaboration experience limits a deeper cultural fit assessment.
Soft Skills & Operational Fit
The resume indicates a proactive approach to learning and application through diverse projects. The candidate's experience as an intern suggests an ability to contribute to ongoing workstreams and integrate ML solutions. However, without direct assessment data on soft skills or operational fit, a comprehensive evaluation is not possible.