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Sree Sai Preetham Nandamuri

@sreesaipreethamnanda

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Generative AI and Data scientist driving production ML and scalable solutions.

United States

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What I'm looking for

I seek a role building production-grade generative AI and ML systems with strong MLOps, collaborative teams, and opportunities to scale models and infrastructure.

I am a Generative AI and Data Scientist with hands-on experience fine-tuning LLaMA-family models, building RAG systems, and deploying production ML services on cloud platforms. I have delivered measurable improvements in model relevance, retrieval speed, and inference latency while applying prompt engineering and vector search to real-world knowledge workflows.

My background includes end-to-end ML system design across agriculture and industrial domains — from synthetic data generation and ensemble forecasting to computer vision and real-time inference. I combine strong engineering practices (containerization, MLOps, AWS) with research-grade modeling (transformers, diffusion, CNNs) to ship robust, cost-efficient solutions that scale.

Experience

Work history, roles, and key accomplishments

Current

Data Scientist - Generative AI

Current

Visual Technologies

Jul 2025 - Present (11 months)

Fine-tuned domain-specific LLaMA 2 models with memory-efficient PEFT (LoRA, QLoRA), improving relevance scores by 15% and implemented RAG with FAISS and OpenAI embeddings to boost retrieval speed by 25%.

LLaMA 2 LoRA QLoRA Faiss Embeddings GPT 4 Prompt Engineering Mistral LoRAs

Associate Data Scientist - DevOps

Yara Digital Farming

Sep 2022 - Jul 2023 (10 months)

Automated classification of 5,000+ agricultural PDFs with fine-tuned BERT achieving 92% accuracy, deployed FastAPI on AWS EKS/SageMaker reducing latency from 3s to 1.2s, and generated 25,000+ synthetic images to cut data costs by $50K+.

BERT SpaCy NLTK fastAPI Sagemaker Docker PyTorch Diffusion Models

Associate Engineer - Data Science

Yara Digital Farming

Aug 2021 - Aug 2022 (1 year)

Built a stacked ensemble (XGBoost, Random Forest) reducing MAE by 18%, improved fertilizer dosing by 22% via ANN, and optimized SQL queries on 5M+ records to speed reporting by 30%.

XGBoost Random Forest Neural Networks Bayesian Optimization SQL MLFlow T SNE

Data Science Intern

Movidu

Aug 2020 - Feb 2021 (6 months)

Developed real-time COVID-19 compliance system using transfer learning with ResNet-50 achieving 94% mask-detection accuracy and delivered recommendation models with 90%+ accuracy for personalized learning.

ResNet50 Transfer Learning ResNet 50 CNN Computer Vision PyTorch Recommendation Systems