About
AI Engineer with over 2 years of experience in developing, fine-tuning, and deploying machine learning models, with a strong focus on Generative AI, LLMs, and deep learning. Possessing expertise across the end-to-end AI model lifecycle, including data preprocessing, training, hyperparameter tuning, fine-tuning (LoRA, QLoRA), and production deployment. Proficient in deep learning frameworks (PyTorch, TensorFlow), LLM optimization (RLHF, DPO), vector databases (FAISS, ChromaDB), and cloud-based AI deployment. Driven by a passion for improving model efficiency, reducing hallucinations, and advancing AI applications through cutting-edge research.
Work
→
Summary
Led the development and deployment of advanced LLM solutions for banking automation, focusing on improving accuracy and efficiency.
Highlights
Developed, fine-tuned, and deployed LLMs for banking automation, improving AI response accuracy by 30%.
Built and optimized custom RAG pipelines using FAISS and ChromaDB, significantly enhancing retrieval efficiency.
Trained and fine-tuned transformer models (LLaMA, GPT) using LoRA and QLoRA, resulting in reduced model memory usage.
Integrated AI models with FastAPI and deployed on AWS SageMaker for seamless real-time interactions.
Conducted comprehensive LLM evaluations using perplexity scores, BLEU, ROUGE, and human feedback loops to drive continuous improvement.
→
Summary
Focused on engineering and optimizing AI-powered code generation models, ensuring high accuracy and reliability.
Highlights
Engineered and optimized AI-powered code generation models, increasing accuracy by 50%.
Fine-tuned models for specialized tasks including code completion, bug detection, and code refactoring.
Implemented Reinforcement Learning from Human Feedback (RLHF) for model evaluation, ensuring correctness and reducing bias in AI-generated code.
Automated model training and deployment workflows utilizing Hugging Face Spaces and AWS Lambda.
→
Summary
Contributed to data-driven solutions, including developing predictive models and enhancing recruitment processes through AI.
Highlights
Developed and deployed a salary prediction model using Scikit-learn, improving accuracy by 15%.
Processed and analyzed over 100,000 employee records using Pandas and SQL, applying robust feature engineering techniques.
Built a resume screening system utilizing NLP techniques (TF-IDF, Word2Vec), significantly improving recruitment efficiency.
Collaborated with engineering teams to deploy AI models and meticulously documented findings for continuous improvement.
Languages
English
Fluent
Skills
Programming Languages
Python.
Machine Learning Frameworks
PyTorch, TensorFlow, Scikit-learn, HuggingFace Transformers.
Machine Learning Concepts
Supervised Learning, Unsupervised Learning, Deep Learning, Transfer Learning, Reinforcement Learning.
LLM Optimization
Fine-tuning (LoRA, QLoRA, PEFT), Model Distillation, Quantization (GPTQ, AWQ), Prompt Engineering, RLHF, DPO.
Generative AI Models
OpenAI, Meta AI (LLaMA), Groq, Mistral, Stable Diffusion, SpeechT5, Nvidia NIM, Crew AI.
Databases & Deployment
FAISS, ChromaDB, AstraDB, Weaviate, SQL Server, MySQL, Git, HuggingFace Spaces, AWS SageMaker, AWS Lambda, FastAPI, Streamlit, Triton Inference Server.
NLP & Data Processing
LangChain, Pandas, SQL, TF-IDF, Word2Vec, Hugging Face Datasets, Data Augmentation, Natural Language Processing, Sentiment Analysis.