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AI Engineer with less than a year in Machine Learning & LLM Development
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AI/ML Engineer experienced in designing and building production-grade Al solutions, including end-to-end machine learning pipelines, LLM-powered systems, and intelligent agentic workflows. Skilled in Python, deep learning, NLP, and modern LLM frameworks, with hands-on expertise in CrewAl, and multi-agent automation. Proficient in architecting and deploying RESTful APIs using Python/FastAPI, integrating Al models into scalable backend services, and delivering reliable, real- world applications.
Nalla Narasimha Reddy Education Society's Group of Institutions, Hyderabad
B. Tech · Artificial Intelligence and Machine Learning
August 1, 2021 – June 30, 2025
SriChaitanya Junior College, Hyderabad
Intermediate (MPC)
June 1, 2019 – May 31, 2021
Dhanush Softech
AI/ML Trainee
August 1, 2025 – April 1, 2026
India
AI-Powered Daily News Digest Agent
June 1, 2026 – Present
Built an 8-node LangGraph pipeline fetching up to 50 articles/source from 2 sources (NewsData.io + RSS), deduplicating with SHA-1 + difflib (0.92 threshold), cutting raw pools by ~70%. Integrated Groq LLaMA 3.3 70B for relevance scoring (1-10 scale, threshold 6.5) and 3-bullet summarization, selecting a curated top-5 newsletter per user daily. Built multi-user PostgreSQL subscription system with per-user APScheduler cron jobs; delivered HTML newsletters via Google OAuth2 Gmail API with rotating logs (2MB × 5 backups).
View ProjectPersonalized Customer Support ChatBot
June 1, 2026 – Present
Developed an Al-powered e-commerce chatbot using LangChain and Gemini Pro through VertexAl, improving customer query resolution speed across product, returns, and order-tracking workflows. Implemented dynamic SQL generation with LangChain + ChromaDB, increasing database answer accuracy and reducing manual lookup effort for support teams. Designed a responsive Streamlit Ul with secure authentication, profile handling, and conversation history, enhancing user engagement and boosting task completion rate.
View ProjectSign Language Recognition Using LSTM
June 1, 2026 – Present
Built an LSTM-based gesture recognition model (TensorFlow/Keras) achieving 90%+ accuracy across 10 gesture classes, improving accessibility for speech- and hearing-impaired users. Extracted keypoints from video frames using MediaPipe and OpenCV, enabling real-time gesture recognition with high accuracy (over 90%) for sequential hand movement data. Developed a scalable video-processing backend in Python, improving system inference speed by ~30% and ensuring stable real-time performance.
View ProjectGenAl
Nvidia Academy
June 1, 2026 – Present
AWSCloud
AWSAcademyGraduate
June 1, 2026 – Present
Python
HackerRank
June 1, 2026 – Present
MachineLearning
Stanford University (Coursera)
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project portfolio showcases a strong interest in applying AI to solve real-world problems, from accessibility (Sign Language Recognition) to daily utility (News Digest Agent) and customer service (ChatBot). This diversity, coupled with an internship in a relevant field and multiple certifications, indicates a proactive and continuous learning mindset. The alignment with an 'AI Engineer' target role is strong, demonstrating a passion for the domain and a breadth of relevant skills and technologies. The candidate appears to be a self-starter with a drive for practical application of AI.
Soft Skills & Operational Fit
The candidate demonstrates strong problem-solving skills through complex project implementations. The ability to work with diverse technologies and integrate multiple systems suggests adaptability and a structured approach to development. The focus on improving accessibility and customer support indicates a user-centric mindset. However, without direct assessment data, specific soft skills like teamwork, communication, and stress handling cannot be definitively evaluated.