Shreerama is an AI/ML Engineer experienced in LLMs, RAG pipelines, and Generative AI. Skilled in NLP, image processing, and end-to-end AI deployment, he builds real-time AI solutions using Python, PyTorch, and TensorFlow. Shreerama has published an IEEE research paper and contributed as a peer reviewer for NetACT 2025.
Built a real-time NOTAM dashboard using LLMs, RAG, and Qwen model for aviation alerts.
Integrated multi-source data pipelines and implemented citation-backed responses.
Optimized prompt logic and deployed scalable RESTful APIs.
Built and deployed AI-powered solutions using Generative AI, LLMs, and RAG pipelines.
Developed intelligent systems for real-world applications, optimizing model performance and integrating custom AI models into scalable back-end architectures.
Deployed advanced Machine Learning solutions in a startup environment.
Improved the HiFiC model for high-quality, lossless image compression.
Enhanced Deep Learning performance through model tuning and error resolution.
Developed a GAN model aimed at enhancing image compression while preserving high visual quality.
Conducted data preprocessing, including resizing and normalization, to prepare a diverse dataset of images for model training.
Evaluated model performance using key metrics such as Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM), ensuring effective compression without significant quality loss.
Collaborated with a multidisciplinary team to troubleshoot model issues, optimizing performance and gaining valuable insights into real-world AI applications.
Fine-tuned LLaMA 3.1 using LoRA and domain-specific datasets to achieve high contextual accuracy for cybersecurity and ethical hacking queries, improving response accuracy for Kali Linux commands by 20%. Developed a web interface, integrating advanced NLP APIs, deployed the model on Ollama server, optimizing query processing latency by 30%. Tech stack: LLMs, Generative AI, AI development.
Designed an AI-powered documentation assistant using advanced RAG with LangChain, LLaMA 2, and Nomic Embed Text to enable real-time query resolution and contextual awareness. Developed and deployed an interactive application using Streamlit, integrated with the Ollama server. Tech stack: RAG, LLaMA 2, LangChain, Generative AI, AI development, Streamlit, NLP.
Developed an AI-driven voice assistant using OpenAI API and Speech Recognition. The application processes spoken questions and provides answers in both voice and text formats. Tech stack: NLP, Speech Recognition, API integration, deployment.
Implemented and optimized Deep Learning models for melanoma detection, focusing on hyperparameter tuning and performance evaluation. Tech stack: Deep Learning, Image Classification, Hyperparameter Tuning.
