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I'm Navyasree Kamireddy, a data scientist and AI engineer based in Houston. I have over three years of experience across the full software development lifecycle, focusing on machine learning, AI, and statistics. I enjoy turning complex data into practical insights that inform business decisions, and I thrive on building models, creating visualizations, and collaborating with cross-functional teams on real-world problems. I’m passionate about leveraging innovative AI tools to drive impact in fast-paced environments. I continuously learn and adapt to new technologies, and I’m eager to contribute to innovative teams tackling challenging AI initiatives with scalable, data-driven solutions.…I'm Navyasree Kamireddy, a data scientist and AI engineer based in Houston. I have over three years of experience across the full software development lifecycle, focusing on machine learning, AI, and statistics. I enjoy turning complex data into practical insights that inform business decisions, and I thrive on building models, creating visualizations, and collaborating with cross-functional teams on real-world problems. I’m passionate about leveraging innovative AI tools to drive impact in fast-paced environments. I continuously learn and adapt to new technologies, and I’m eager to contribute to innovative teams tackling challenging AI initiatives with scalable, data-driven solutions.

Navyasree Kamireddy

AI Engineer, Data Scientist, Data Analyst, +3

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I'm Navyasree Kamireddy, a data scientist and AI engineer based in Houston. I have over three years of experience across the full software development lifecycle, focusing on machine learning, AI, and statistics. I enjoy turning complex data into practical insights that inform business decisions, and I thrive on building models, creating visualizations, and collaborating with cross-functional teams on real-world problems. I’m passionate about leveraging innovative AI tools to drive impact in fast-paced environments. I continuously learn and adapt to new technologies, and I’m eager to contribute to innovative teams tackling challenging AI initiatives with scalable, data-driven solutions.…I'm Navyasree Kamireddy, a data scientist and AI engineer based in Houston. I have over three years of experience across the full software development lifecycle, focusing on machine learning, AI, and statistics. I enjoy turning complex data into practical insights that inform business decisions, and I thrive on building models, creating visualizations, and collaborating with cross-functional teams on real-world problems. I’m passionate about leveraging innovative AI tools to drive impact in fast-paced environments. I continuously learn and adapt to new technologies, and I’m eager to contribute to innovative teams tackling challenging AI initiatives with scalable, data-driven solutions.

Available to hire

I’m Navyasree Kamireddy, a data scientist and AI engineer based in Houston. I have over three years of experience across the full software development lifecycle, focusing on machine learning, AI, and statistics. I enjoy turning complex data into practical insights that inform business decisions, and I thrive on building models, creating visualizations, and collaborating with cross-functional teams on real-world problems.

I’m passionate about leveraging innovative AI tools to drive impact in fast-paced environments. I continuously learn and adapt to new technologies, and I’m eager to contribute to innovative teams tackling challenging AI initiatives with scalable, data-driven solutions.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Intermediate

Language

English

Fluent

Work Experience

AI Engineer at Data Robot

February 1, 2025 - Present

Designed GenAI pipelines with LLMs and Retrieval-Augmented Generation (RAG) architectures using vector databases, reducing hallucination by 25% and enhancing predictive analytics for business intelligence workflows. Fine-tuned open-source LLMs via Hugging Face and PEFT for domain-specific tasks, optimizing inference speed and deploying models through APIs in production environments. Performed prompt engineering and A/B testing on LLM variants to improve response quality for client-facing AI applications in automated decision-making. Deployed production-ready AI applications on AWS using Bedrock for foundation models and ECS for containerized deployments, orchestrating serverless workflows with Lambda and S3 for seamless scalability. Analyzed large-scale datasets with Pandas and embeddings to build semantic search features, contributing to more accurate AI-driven insights in business intelligence workflows.

Machine Learning Engineer at Fractal Analytics

May 1, 2021 - November 1, 2023

Developed predictive models leveraging neural networks and time series techniques, with explorations into LLM integrations for enhanced natural language understanding in analytics consulting projects. Built end-to-end ML pipelines including data preprocessing, feature engineering, and model deployment; incorporated embedding-based retrieval methods to support knowledge-intensive applications. Applied clustering and classification algorithms to segment data, laying the groundwork for RAG-like systems that combined structured data with unstructured text for better forecasting in retail and finance domains. Collaborated on cross-team initiatives to evaluate model performance, focusing on accuracy, bias mitigation, and scalability for enterprise AI solutions.

Data Scientist at Standard Chartered

February 1, 2020 - April 1, 2021

Built image data pipelines using Keras preprocessing for deep learning models; expanded to multimodal AI workflows supporting 10+ team members. Developed Python/Pandas and Tableau dashboards with time-series and geo-visualizations; integrated LLM prototypes for automated report summarization serving 25+ analysts. Deployed a Kaggle-based KPI tracking dashboard, improving cross-departmental reporting efficiency by 25%. Architected AWS-based data science infrastructure (S3, EC2, SageMaker) for scalable ML model training and deployment, reducing pipeline runtime by 40%.