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AI Engineer with 2+ years in Machine Learning & Time Series Forecasting
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Seeking a challenging role in Data Science and Machine Learning where I can apply my hands-on experience in time series forecasting, model deployment, and pipeline automation using Python, time series analysis, while continuing to grow in a collaborative and technology-driven environment.
School of Information Technology Bhopal
MTech
August 1, 2019 – June 30, 2021
HCLTech
Post Graduate Engineer Trainee
February 15, 2024 – Present
India
End-to-End Financial Time Series Forecasting
January 1, 2021 – January 1, 2021
Developed an end-to-end financial time series forecasting pipeline by implementing and evaluating classical and hybrid models on historical price data. Processed and engineered features including outlier detection and handling, log transformation, and temporal feature extraction And built multiple forecasting model. Technologies and Tools: Python, Pandas, NumPy, Matplotlib, Seaborn, SARIMAX, Prophet, ARCH, Exponential Smoothing, Time SeriesPreprocessing, Feature Engineering, hybrid forecasting model, volatility modelling.
Energy Consumption Forecasting with MLOps Pipeline
January 1, 2021 – January 1, 2021
Built an end-to-end MLOps pipeline for forecasting energy consumption using time series data.Implemented a hybrid SARIMA + XGBoost model. Automated data preprocessing,model training, and evaluation using Apache Airflow. Containerized the pipeline using Docker. Technologies and Tools: Apache Airflow, MLflow, Docker, SARIMA, XGBoost, Pandas,scikit-learn.
Developed and Deployed a RAG system for Contextual Document Query
January 1, 2021 – January 1, 2021
Developed and deployed RAG system that enables users to interact with large documents (such as PDFs, research papers)by asking natural language questions.The system process and retrieve relevant document sections and uses OpenAI GPT-4 to generate context aware answers, improving the efficiency and accuracy of information retrieval. Technologies and Tools; PyMuPDF,NLTK/spacy,OpenAI embeddings, sentence-transformers, Hugging face,FAISA
A Neural Network Based Diabetes Prediction on Imbalanced Data
January 1, 2021 – January 1, 2021
The diabetes dataset is a binary classification problem where it needs to be analysed whether a patient is suffering from the disease or not on the basis of many available features in the dataset. Technology used: Python,EDA, Data Visualization,Data cleaning Library used:Pandas,NumPy,Seaborn, Matplotlib, Scikit-learn,Model Selection, Imblearn. Done data preprocessing such as handle outliers and missing values, feature scaling,feature selection, oversampling techniques.Apply Multiplayer Perceptron(MLP) as classifier to make prediction and achieve accuracy 0.83 with f- measure 0.87.
Complete Data Science and Machine Learning Bootcamp 2022
Udemy
January 1, 2022 – Present
Machine Learning Model Deployment using Flask
Great Learning Academy
June 1, 2021 – Present
Recent Advances in Artificial Intelligence
NIT Uttarakhand and SLIET Longowal
July 1, 2020 – Present
Machine Learning and Deep Learning using Python
RGPV, Bhopal
June 1, 2020 – Present
Cultural Fit Analysis
The candidate's projects demonstrate a strong interest in Artificial Intelligence and Generative AI, aligning well with an AI Engineer role. The diversity of projects, from financial forecasting to RAG systems and medical predictions, shows a broad application of AI/ML skills. The continuous learning through certifications and participation in training programs indicates a proactive and growth-oriented mindset, which is a good cultural fit for dynamic tech environments.
Soft Skills & Operational Fit
The candidate's resume highlights collaboration with product, engineering, and data teams, suggesting an ability to work in a cross-functional environment. The emphasis on documentation and reporting indicates good communication and organizational skills. The project descriptions show initiative and an end-to-end approach to problem-solving.