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AI Engineer with less than a year in Machine Learning & Deep Learning
AI is analyzing your overall score…
Identifying your key strengths…
Evaluating your skill match against the job requirements…
Assessing your cultural and operational fit
Aspiring Machine Learning Engineer skilled in Python, Deep Learning, LLM fine-tuning, and RAG systems. Experienced in building predictive models and AI-driven applications using TensorFlow, PyTorch, scikit-learn, and Hugging Face Transformers to solve real-world problems across multiple domains
SRM IST Kattankulathur
B.Tech · Electronics & Communication Engineering
August 1, 2022 – June 30, 2026
B.V.B's Sri Venkateswara Vidyalaya
Class X · CBSE
N/A – May 31, 2020
Raju Jr College
Class XII · MPC
N/A – May 31, 2022
Cognifyz Technologies
Machine Learning Intern
January 1, 2026 – Present
India
F1 Strategist AI (LLM/Generative AI)
September 1, 2025 – June 30, 2026
Built a RAG-based AI system using FastF1 and ChromaDB as a vector store to semantically retrieve Formula 1 telemetry and race data; integrated OpenRouter to generate data-grounded pit-stop strategy recommendations via an LLM, enabling real-time race analysis and strategic decision support through a FastAPI web app deployed on Render.
Healthcare AI Chatbot (LLM Fine-tuning)
July 1, 2025 – June 30, 2026
Fine-tuned LLaMA on curated medical datasets for healthcare-oriented question answering, clinical summarization, and diagnostic assistance; applied domain-specific prompt engineering, instruction tuning, and quantitative evaluation metrics to optimize model performance across diverse patient query types.
Breast Cancer Detection (Machine Learning)
May 1, 2025 – June 30, 2026
Engineered a supervised classification model on the Wisconsin Breast Cancer dataset; applied feature engineering, dimensionality reduction, and confusion matrix analysis to evaluate and compare multiple classifiers including Logistic Regression, SVM, and Random Forest, achieving 96% accuracy on held-out test data.
Bank Customer Churn Prediction (Deep Learning)
March 1, 2025 – June 30, 2026
Built and trained an ANN model on customers' financial, demographic, and account activity data to predict churn likelihood; performed systematic hyperparameter tuning across learning rate, dropout, and layer depth, achieving a 13% improvement in prediction performance and reducing false negatives critical for customer retention strategies.
Claude in Action
Antropics
January 1, 2026 – Present
Data Visualization
TATA
January 1, 2026 – Present
AI A-Z: AI, Agentic and Generative AI
SuperDataScience
January 1, 2026 – Present
Machine Learning A-Z: AI, Python & R
SuperDataScience
January 1, 2025 – Present
Cultural Fit Analysis
The candidate shows a strong interest in AI/ML through personal projects and certifications, aligning well with an AI-focused role. The diversity of projects (F1 strategy, healthcare chatbot, churn prediction, breast cancer detection) indicates adaptability and a broad interest in applying AI to different domains. The remote internship suggests a degree of self-motivation and ability to work independently. The candidate is currently pursuing a B.Tech, indicating a proactive approach to learning and skill development.
Soft Skills & Operational Fit
The candidate's project descriptions indicate an ability to work on complex problems independently and apply structured approaches (e.g., hyperparameter tuning, evaluation metrics). The internship experience suggests collaboration and delivery of data-driven solutions. However, without direct assessment data, specific soft skills like leadership, conflict resolution, or advanced communication in a team setting cannot be fully evaluated.