AI Engineer with less than a year in NLP, LLMs, and RAG architectures
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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 – June 30, 2026
SkillDzire
Python Developer
June 1, 2026 – Present
India
Intelligent Question Answering System using LLM & RAG
June 1, 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.
Google Cloud Computing Foundations & Generative AI
Google Cloud Computing
June 1, 2026 – Present
Software Engineering Course
NPTEL
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's academic project on LLM & RAG, coupled with certifications in Generative AI and Google Cloud, aligns well with an AI Engineer role. The internship as a Python Developer provides some industry exposure. However, the overall project diversity is limited to a single academic project, and the experience level is entry-level, which might require significant mentorship for a senior role. The breadth of skills is good for an AI role, but the depth across various domains beyond the primary project is not explicitly demonstrated.
Soft Skills & Operational Fit
The candidate's project description demonstrates an ability to work on complex technical problems, breaking them down into manageable components (preprocessing, chunking, indexing, API integration, deployment). The use of Docker and CI/CD suggests an understanding of operational best practices for scalable AI systems. However, without direct assessment data on collaboration or communication, specific soft skills cannot be fully evaluated.