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ML Engineer with less than a year in AI/ML Pipelines & NLP
Final-year B.Tech (AI & ML) student with hands-on internship experience at Cognifyz Technologies and strong fundamentals in machine learning, deep learning, NLP, and computer vision. Experienced in building end-to-end AI/ML pipelines spanning CNN-based vision systems, transformer-based NLP, healthcare AI, and data-driven recommendation systems. Seeking roles in ML Engineering or NLP Engineering to contribute to scalable, production-grade AI systems.
Malla Reddy University
B.Tech · Computer Science Engineering (AI & ML)
August 1, 2022 – June 30, 2026
Narayana Junior College
Intermediate (MPC)
June 1, 2020 – May 31, 2022
Unique High School
SSC
June 1, 2019 – May 31, 2020
Cognifyz Technologies
Machine Learning Intern
May 25, 2026 – June 25, 2026
India
Fire Detection System using Deep Learning
June 1, 2026 – Present
Designed and trained a CNN model to detect fire in real-time video streams, achieving 92%+ accuracy. Built an end-to-end pipeline covering dataset curation, image annotation, augmentation, model training, and inference optimization (reducing false-positive rate by ~18%). Deployed inference on a CPU-only machine at ~15 FPS using OpenCV.
Text Summarization Model
June 1, 2026 – Present
Developed both extractive (TF-IDF ranking) and abstractive (sequence-to-sequence with attention) summarization models on news article datasets. Fine-tuned a pre-trained BERT-based encoder for abstractive generation, achieving ROUGE-1: 0.41, ROUGE-L: 0.38. Benchmarked multiple approaches and documented trade-offs.
Voice-Activated Calculator
June 1, 2026 – Present
Built a voice-controlled arithmetic application using the SpeechRecognition library and NLTK for intent classification and entity extraction. Improved recognition accuracy from ~74% to 91% by adding audio pre-processing (noise reduction, normalization) and post-processing (spell-correction, regex normalization) stages. Designed a modular NLP pipeline.
Child Adoption Management System
June 1, 2026 – Present
Developed a responsive full-stack web application managing 500+ user records and adoption case workflows with role-based access control. Designed a normalized relational database schema (3NF) and optimized SQL queries (reducing average query time by ~30% via indexing). Demonstrated secure form handling and backend system design.
Multiple Disease Prediction System with AI Health Assistant
June 1, 2026 – Present
Engineered a unified prediction system covering 3 diseases (Diabetes, Heart Disease, Parkinson’s Disease) using dedicated Scikit-learn classifiers (SVM, Logistic Regression, Random Forest). Achieved Diabetes: 87% accuracy, Heart Disease: 89% accuracy, and Parkinson’s: 91% accuracy. Integrated a rule-based AI chatbot for symptom input and query routing. Built an end-to-end pipeline including data preprocessing, SMOTE, feature importance, model serialization, and a Streamlit web UI. Evaluated models using confusion matrix, precision, recall, F1-score, and ROC-AUC.
Trailhead
Salesforce
June 1, 2026 – Present
AWS Cloud Foundations
AWS Academy
June 1, 2026 – Present
Database
Oracle
June 1, 2026 – Present
Machine Learning Specialization
Coursera / DeepLearning.AI
June 1, 2026 – Present
Natural Language Processing with Classification and Vector Spaces
Coursera
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's project diversity, covering web development, NLP, computer vision, and healthcare AI, indicates a broad interest and adaptability, which can be a positive for cultural fit in dynamic environments. The target role of ML Engineer aligns well with the candidate's academic background and project focus. The breadth of skills, including both ML/DL and some full-stack development (PHP, SQL), suggests a versatile individual. However, the experience is primarily academic, and the internship is very short, so exposure to corporate culture, long-term project cycles, and cross-functional team dynamics is limited. The certifications in AWS and Salesforce indicate a proactive approach to learning industry-relevant tools.
Soft Skills & Operational Fit
The candidate demonstrates problem-solving skills through iterative optimization in projects (e.g., reducing false positives, improving recognition accuracy). Team collaboration is mentioned as a soft skill, but specific examples of collaboration in projects are limited. Detail-oriented thinking is evident in thorough model evaluation and documentation of trade-offs. The academic nature of most projects suggests a strong learning aptitude and ability to work independently on defined tasks. The short internship provides some exposure to a more structured operational environment.