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AI Engineer with less than a year in ML and GenAI applications, focusing on RAG systems, NLP, and co
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
AI Engineer with hands-on experience building end-to-end ML and GenAI applications, from data pipelines to deployed APIs. Focused on RAG systems, NLP, and computer vision using Python, LangChain, LLMs, and containerized deployment.
Jawaharlal Nehru College | RTMN University
Bachelor of Science · Computer Science
August 1, 2022 – June 30, 2025
Infotact Solutions
Data Science Intern
July 1, 2025 – August 31, 2025
India
RAG Document Assistant
June 1, 2026 – Present
Built a RAG system for natural language querying over PDF documents with cited answers. Engineered pipeline: PDF parsing - chunking - embeddings - FAISS indexing - LLM response. Returns answers with source page references to reduce hallucination. Deployed as Dockerized FastAPI service; handles 200+ page docs in sub-3s response.
Real-Time Surface Defect Detector
June 1, 2026 – Present
Trained YOLOv8 on NEU Defect Dataset (1,800 images, 6 classes) for industrial inspection. Applied augmentation (rotation, noise, mosaic) to handle class imbalance - 3x minority samples. Achieved mAP@0.5 of 0.82 across all defect categories on held-out test set. Built FastAPI endpoint returning annotated images with bounding boxes in <500ms.
AI Resume-Job Fit Analyzer
June 1, 2026 – Present
Built tool that parses resumes + JDs, performs skill gap analysis, generates fit reports. Designed multi-step LangChain pipeline: extraction → matching → scoring → report. Structured prompts produce consistent JSON output, validated across 5+ job domains. Deployed on Streamlit with drag-drop upload, scoring, and downloadable PDF reports.
Cultural Fit Analysis
The candidate's portfolio demonstrates a strong interest in applied AI, with projects spanning NLP (RAG, Resume Analyzer), Computer Vision (Defect Detector), and general ML deployment. This diversity aligns well with an AI Engineer role that often requires adaptability across different domains. The focus on building deployable solutions suggests a product-oriented mindset, which is a good cultural fit for many engineering teams. The candidate is still pursuing their bachelor's degree, indicating a strong learning curve and potential for growth.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a proactive and results-oriented approach, focusing on practical applications and deployment. The internship experience suggests an ability to collaborate and work within structured workflows. However, without direct assessment data, specific soft skills like problem-solving under pressure or team communication cannot be fully evaluated.