ML Engineer with 2+ years in Machine Learning & Generative AI
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Innovative Python Developer specializing in Machine Learning, Deep Learning, and Generative AI. Proficient in building end-to-end AI pipelines and NLP solutions using Python, TensorFlow, PyTorch, and LangChain. Strong track record of developing RAG-based chatbots and developing hybrid deep learning models to solve complex technical challenges.
Anurag University
Bachelor of Technology · Civil Engineering
August 1, 2021 – June 30, 2024
Krishna Devaraya Govt. Polytechnic College
Diploma · Civil Engineering
August 1, 2018 – June 30, 2021
T S Model School
10th Class
June 1, 2017 – May 31, 2018
CS Software Solution
ML Engineer
May 1, 2024 – Present
India
AI-Powered Healthcare Assistant (RAG-Based Chatbot)
May 1, 2026 – June 1, 2026
• Built a domain-specific medical AI assistant using Retrieval-Augmented Generation (RAG) architecture. • Integrated FAISS for efficient vector-based document retrieval from medical literature (Gale Encyclopedia of Medicine). • Powered response generation using Mistral-7B-Instruct-v0.3 via Hugging Face Transformers and LangChain orchestration.
COVID-19 Classification using Hybrid VGG-16 + ResNet-50 with CRO
May 1, 2026 – June 1, 2026
• Designed a hybrid deep learning model combining VGG-16 and ResNet-50 architectures for chest X-ray COVID-19 detection. • Applied Coral Reef Optimization (CRO) for hyperparameter tuning, achieving 95.17% accuracy, 97.67% precision, 96.55% F1-score.
Drought Prediction using Bi-LSTM Attention Mechanism with IHGS Optimizer
May 1, 2026 – June 1, 2026
• Developed a drought forecasting model using Bi-LSTM with Attention Mechanism and Improved Hunger Games Search (IHGS) optimizer. • Incorporated meteorological indicator (SPEI); model outperformed baselines in R2 score, MSE, and RMSE metrics.
Heart Sound Identification using GAN + Vision Transformer (ViT)
May 1, 2026 – June 1, 2026
• Designed a hybrid GAN + ViT framework for automated cardiac auscultation, classifying heart sounds into normal, murmur, and artifact categories using clinical datasets. • Applied GAN-based data augmentation to tackle data scarcity; extracted MFCCs, spectral contrast, chroma features as inputs to ViT's self-attention mechanism for spectrogram analysis. • Achieved 91.45% accuracy, 91.35% precision, 91.45% recall, 91.36% F1-score demonstrating strong clinical applicability.
Python for Data Science
IBM
June 1, 2026 – Present
Introduction to Machine Learning
Great Learning
June 1, 2026 – Present
Basics of Python
Infosys Springboard
June 1, 2026 – Present
Basics of MySQL
Great Learning
June 1, 2026 – Present
Cultural Fit Analysis
The candidate's projects demonstrate a strong interest in applying AI to real-world problems, particularly in healthcare and environmental domains, which aligns with an innovative and impact-driven culture. The breadth of technologies used across projects (RAG, GAN, ViT, Bi-LSTM, various optimizers) indicates a proactive learning attitude. However, the lack of team-based projects or explicit collaboration mentions makes it hard to fully assess cultural fit in a collaborative engineering environment. The recent entry into the professional ML field suggests a need for mentorship and integration into established team workflows.
Soft Skills & Operational Fit
The candidate's project descriptions indicate a problem-solving mindset and an ability to apply advanced ML techniques to diverse domains (healthcare, climate, medical diagnostics). The short tenure at CS Software Solution (current role) makes it difficult to assess long-term operational fit or collaboration skills. The education in Civil Engineering, while not directly related to ML, suggests an analytical foundation, but also a career transition that might require additional domain-specific mentorship.