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ashuguptahere committed Nov 24, 2024
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# Data Scientist/AI Engineer
# Data Scientist & AI Engineer

## 🛠️ SKILLS:
- **Technical Skills**: Proficient in ML, DL, AI, GenAI, Data Science, Data Analysis, Python, Git/GitHub, MySQL, T-SQL Databases, Computer Vision, Video Analytics, NLP, RAG, EDA, C/C++, DSA, Linux, PowerBI, MLOps, Azure and AWS
- **Libraries Known**: PyTorch, Tensorflow, Keras, OpenCV, Scikit-Learn, NumPy, Pandas, NLTK, OpenAI, Streamlit, Flask, LangChain
- **Exposure to**: C#, Java, HTML, CSS, JavaScript (ECMAScript), Mojo, Rust
- **Languages Known**: English, Hindi, Portuguese (beginner)
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## 💼 EXPERIENCES:
**Data Scientist & AI Engineer @ Quantum Leap (Dec 2023 - Present)**
**Data Scientist & AI Engineer** | _(Dec 2023 - Present)_
**Quantum Leap** | _Setúbal, Portugal (Remote)_
- Leading and delivering impactful initiatives in Computer Vision Projects. Developing a system for Human Activity Recognition/Behaviour Analysis contributing to improved safety protocols and preventive measures for Retail Loss Prevention
- Engineered backend solutions to streamline and analyze incoming streams, enabling real-time irregular behaviours detection
- Led the development of a fully localized Llama-3 70B LLM model tailored for a law firm client
- Built a sophisticated chat interface allowing the client to interact with their data, facilitating quick identification of relevant documents and specific information within vast repositories of PDFs and text files

**Software Engineer @ MAQ Software (Dec 2022 - Dec 2023)**
**Software Engineer** | _(Dec 2022 - Dec 2023)_
**MAQ Software** | _NOIDA, India_
- Spearheaded and successfully led initiatives in Retrieval Augmented Generation, contributing to advancement of content generation techniques
- Applied state-of-the-art LLMs like OpenAI’s GPT3.5 & GPT4 and Llama 2 for RAG applications
- Optimized client’s legacy system performance using Python, specifically leveraging NumPy and Pandas for calculations, achieving a substantial reduction in execution time from 7 minutes to 10 seconds, making application 42x faster
- Deployed end-to-end production-grade solutions on Azure, utilizing services like Function App, Web App, Cognitive Service (Search Index/Indexer), Blob Storage, SQL Databases and Speech Service
- Implemented High-Impact and robust Recommendation System handling a massive data of 1 million users

**System Engineer @ TCS (Aug 2021 - Dec 2022)**
**System Engineer** | _(Aug 2021 - Dec 2022)_
**Tata Consultancy Services (TCS)** | _NOIDA, India_
- Led and executed numerous Data Analysis and Data Science Projects
- Proficient in Python development, showcasing skills in backend development for robust and scalable applications

---
## 🧑‍💻 INTERNSHIPS:

**Data Science Intern @ VerSe Innovation (Jul 2021 - Aug 2022)**
**Data Science Intern** | _(Jul 2021 - Aug 2022)_
**VerSe Innovation** | _Bengaluru, India_
- Engaged in challenging Computer Vision endeavors, including tasks encompassing Image and Video Classification

**AI Trainer @ CETPA InfoTech Pvt. Ltd. (Jan 2020 - Feb 2020)**
**AI Trainer** | _(Jan 2020 - Feb 2020)_
**CETPA InfoTech Pvt. Ltd.** | _NOIDA, India_
- Educated and taught college students in their 3rd and 4th years on a wide array of AI topics

## 🛠️ SKILLS:
- **Technical Skills**: Proficient in ML, DL, AI, GenAI, Data Science, Data Analysis, Python, Git/GitHub, MySQL, T-SQL Databases, Computer Vision, Video Analytics, NLP, RAG, EDA, C/C++, DSA, Linux, PowerBI, MLOps, Azure and AWS
- **Libraries Known**: PyTorch, Tensorflow, Keras, OpenCV, Scikit-Learn, NumPy, Pandas, NLTK, OpenAI, Streamlit, Flask, LangChain
- **Exposure to**: C#, Java, HTML, CSS, JavaScript (ECMAScript), Mojo, Rust
- **Languages Known**: English, Hindi, Portuguese (beginner)

---
## 🎓 EDUCATIONS:
- **Bachelor of Technology, CSE** _from_ Shaheed Bhagat Singh State Technical Campus (Aug 2016 - Jul 2020)

---
## 📂 PROJECTS:
- [AI Cover Letter Generator](https://github.com/ashuguptahere/cover-letter-gen)
- Simple and intuitive web interface made using Gradio with real-time Cover Letter Generation
- Usage of Llama 3.2 model from Ollama
- Usage of `uv` package manager for package dependency
- [POLAR Dataset Conversion and YOLO11 Training](https://github.com/ashuguptahere/POLAR-yolo-conversion-and-train)
- Trained YOLO11 model on POLAR dataset for human activity detection
- Usage of `uv` package manager for package dependency
- [YOLO Gradio App](https://github.com/ashuguptahere/yolo-gradio-app)
- Simple and intuitive web interface made using Gradio for training any Ultralytics model from WebUI
- Usage of `uv` package manager for package dependency
- [Real-Time Sign Language Detection](https://github.com/ashuguptahere/sign-language-detection)
- Achieved a validation accuracy of 99.348% and a minimal loss of 0.03621 on ResNet102
- Dataset Used: [ASL Alphabet](https://www.kaggle.com/grassknoted/asl-alphabet)
- [Human Activity Recognition](https://github.com/ashuguptahere/video-classification-ucf101)
- Conducted Video Classification on two distinct massive datasets, UCF11 and UCF101
- Demonstrated a robust validation accuracy ranging from 90% to 95% on ResNet102
- Dataset Used: [UCF101](https://www.kaggle.com/ashuguptahere/video-classification-ucf101), [UCF11](https://www.kaggle.com/ashuguptahere/video-classification-ucf11)
**[AI Cover Letter Generator](https://github.com/ashuguptahere/cover-letter-gen)**
- Simple and intuitive web interface made using Gradio with real-time Cover Letter Generation
- Usage of Llama 3.2 model from Ollama
- Usage of `uv` package manager for package dependency

**[YOLO Gradio App](https://github.com/ashuguptahere/yolo-gradio-app)**
- Simple and intuitive web interface made using Gradio for training any Ultralytics model from WebUI
- Usage of `uv` package manager for package dependency

**[POLAR Dataset Conversion and YOLO11 Training](https://github.com/ashuguptahere/POLAR-yolo-conversion-and-train)**
- Trained YOLO11 model on POLAR dataset for human activity detection
- Usage of `uv` package manager for package dependency

**[Sign Language Classification](https://github.com/ashuguptahere/sign-language-classification)**
- Classification of different sign language alphabet using YOLO11
- Dataset Used: [ASL Alphabet](https://www.kaggle.com/grassknoted/asl-alphabet)

**[Real-Time Sign Language Detection](https://github.com/ashuguptahere/sign-language-detection)**
- Achieved a validation accuracy of 99.348% and a minimal loss of 0.03621 on ResNet102
- Dataset Used: [ASL Alphabet](https://www.kaggle.com/grassknoted/asl-alphabet)

**[Human Activity Recognition](https://github.com/ashuguptahere/video-classification-ucf101)**
- Conducted Video Classification on two distinct massive datasets, UCF11 and UCF101
- Demonstrated a robust validation accuracy ranging from 90% to 95% on ResNet102
- Dataset Used: [UCF101](https://www.kaggle.com/ashuguptahere/video-classification-ucf101), [UCF11](https://www.kaggle.com/ashuguptahere/video-classification-ucf11)

---

## 📜 CERTIFICATIONS:
- [Coding Blocks - Algo++](https://drive.google.com/file/d/1hiyOK3MlFXi0583gJTVbafHLXmegdTvS/view?usp=sharing)
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- [Microsoft - Fundamental of ML](https://drive.google.com/file/d/1SSPmCUa9mdKV8BjQj_RvWyFiPn-vGKpz/view?usp=sharing)
- [ISOEH - Machine Learning](https://drive.google.com/file/d/1Pk-nYnK60L8UbcFB3BfSbxoZ3xy0Froh/view)

---

## 🏆 ACHIEVEMENTS
- [Gate 2021](https://drive.google.com/file/d/1ARz_BLLcELOdsD-tSp_IxuJ8UMXhP7_6/view?usp=share_link) cleared with Marks: 25.21 & Gate Score: 339
- Practiced over [190+ DSA questions](https://codolio.com/profile/ashuguptahere) on various platforms

---

## 📬 SOCIALS:
[![LinkedIn](https://img.shields.io/badge/LinkedIn-%230077B5.svg?logo=linkedin&logoColor=white)](https://www.linkedin.com/in/ashuguptahere) [![X](https://img.shields.io/badge/X-black.svg?logo=X&logoColor=white)](https://x.com/hey_its_ashu)

---

Made with ❤️ by [Aashish Gupta](https://github.com/ashuguptahere)

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