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AI Engineer with 1+ years in LLMs & RAG for scalable AI applications.
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Assessing your cultural and operational fit
Computer Science (AI) graduate with hands-on experience in Large Language Models (LLMs), NLP, Retrieval-Augmented Generation (RAG), FastAPI, PyTorch, and Hugging Face. Skilled in building production-ready AI applications, fine-tuning workflows, evaluation pipelines, and scalable inference systems. Passionate about applying Generative AI and machine learning to solve real-world problems.
Dr. MGR Educational and Research Institution
B. Tech · Computer Science and Engineering with AI
August 1, 2021 – June 30, 2025
Bexto10x IT Solution
Associate Data Engineer
November 1, 2025 – May 1, 2026
Hyderābād, Telangana, India
Digisamaskh IT Company
Data Science Intern
June 1, 2025 – December 1, 2025
India
Offline AI Document Assistant with Voice-Enabled RAG
May 1, 2026 – Present
Developed a locally hosted NLP-based RAG system enabling conversational querying of PDF documents through text and voice interfaces while ensuring complete offline data privacy. Integrated Mistral-7B using llama-cpp-python with Hugging Face Embeddings and FAISS for semantic search, document retrieval, and context-aware response generation. Implemented speech-to-text and text-to-speech capabilities using SpeechRecognition and pyttsx3, enhancing accessibility and hands-free document interaction. Built scalable asynchronous document processing and vectorization workflows using Django, Celery, Redis, AJAX, HTML, CSS and JavaScript, delivering real-time chat experiences.
CodeLens | Live | CrewAI
March 1, 2026 – Present
Engineered end-to-end ETL pipelines using PySpark to ingest, transform, and aggregate GitHub repository metadata, 6-agent CrewAI code review system, agent traces, and review metrics, enabling scalable analytics across large codebases. Designed an event-driven data architecture using Kafka, Celery, and Redis for distributed task orchestration, real-time data processing, asynchronous job execution, and high-throughput review workflow management. Developed analytics and observability pipelines leveraging LangSmith traces, repository insights, review outcomes, performance monitoring reports, and engineering productivity insights within a Dockerized microservices architecture.
RAG Based PC Prediction
September 1, 2025 – Present
Developed a RAG-based recommendation system using FAISS and FastAPI to suggest gaming PC configurations from SKU-level hardware catalogs based on user requirements, budget, and performance preferences. Engineered an asynchronous backend using FastAPI, Celery, Redis, async SQLAlchemy, and PostgreSQL for scalable recommendation processing. Integrated LangSmith observability for tracing token usage and implemented Google OAuth + JWT authentication and cross lingual research support through a dedicated Translation Agent.
Cultural Fit Analysis
The candidate's projects demonstrate a strong interest in applying AI to solve real-world problems, from document assistants to code review systems and PC recommendations. This project diversity, coupled with experience in both data engineering and data science, indicates adaptability and a broad technical curiosity. The target role of AI Engineer aligns well with the candidate's project focus on LLMs, RAG, and scalable AI application development. The candidate's education is still ongoing, which might indicate a proactive approach to learning and applying new technologies.
Soft Skills & Operational Fit
The candidate lists Analytical Thinking, Problem Solving, Stakeholder Communication, Attention to Detail, and Team Collaboration as soft skills. Project descriptions indicate practical application of problem-solving and stakeholder communication, particularly in the Associate Data Engineer role and the 'Offline AI Document Assistant' project. The use of Azure DevOps and Git suggests an understanding of operational workflows and collaboration.