AI Engineer with less than a year in NLP, LLMs, and RAG architectures
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Enthusiastic AI & ML Engineer with hands-on experience in NLP, LLMs, RAG architectures, and deep learning frameworks like TensorFlow and PyTorch. Proficient in Python, SQL, Docker, and CI/CD workflows, aiming to build scalable and intelligent AI systems in a dynamic organization.
Jawaharlal Nehru Technological University Anantapur
B. Tech · CSE (Artificial Intelligence)
N/A – Present
Intelligent Question Answering System using LLM & RAG
June 24, 2026 – Present
Built an AI-powered question answering system using Large Language Models (LLMs) and Retrieval-Augmented Generation (RAG). Implemented document embedding and vector similarity search for contextual information retrieval. Designed a pipeline to preprocess, chunk, and index large datasets for efficient semantic search. Integrated LLM APIs to generate context-aware responses with improved factual accuracy. Used SQL databases for structured storage and retrieval optimization. Developed REST APIs for real-time inference and integrated frontend interface. Deployed the application using Docker with CI/CD automation for scalable production deployment. Improved response relevance by implementing cosine similarity and embedding-based ranking.
NPTEL Software Engineering Course
NPTEL
June 1, 2026 – Present
Google Cloud Computing Foundations & Generative AI
Google Cloud
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic project and certifications align well with an AI Engineer role, demonstrating a clear interest and foundational knowledge in the field. The project diversity is limited to one academic project, and there is no professional experience to assess broader adaptability or collaboration in a corporate setting. The skills listed are highly relevant to the target role, indicating a focused career path.
Soft Skills & Operational Fit
The candidate's project description indicates an ability to work on complex technical problems and deploy solutions, suggesting a problem-solving mindset and operational awareness. However, without direct experience or psychometric test results, it's difficult to assess stress handling, team collaboration, or broader work attitude.