I am a Data Science and AI Engineer with hands-on experience designing AI image generation workflows for real-world, production-ready use cases. My work focuses on transforming abstract user inputs into consistent, high-quality visual outputs that meet strict technical constraints rather than just aesthetic goals. I have practical experience with generative image models and advanced prompt engineering, including designing prompts and validation steps that account for real-world printing requirements such as bleed, trim, and safe zones. I approach AI image generation as a workflow problem defining input constraints, guiding models toward predictable layouts, and ensuring outputs are reliable and usable for physical products like labels and packaging. In past projects, I have evaluated and compared generative AI tools for quality, consistency, and production suitability, and documented repeatable processes to make AI outputs scalable and maintainable. I am detail-oriented, comfortable iterating with stakeholders, and focused on delivering results that are ready for real-world deployment rather than experimental demos. I enjoy working at the intersection of AI, design constraints, and practical production needs, and I bring a structured, problem-solving mindset to building AI-powered creative workflows.

Neha Valeti

I am a Data Science and AI Engineer with hands-on experience designing AI image generation workflows for real-world, production-ready use cases. My work focuses on transforming abstract user inputs into consistent, high-quality visual outputs that meet strict technical constraints rather than just aesthetic goals. I have practical experience with generative image models and advanced prompt engineering, including designing prompts and validation steps that account for real-world printing requirements such as bleed, trim, and safe zones. I approach AI image generation as a workflow problem defining input constraints, guiding models toward predictable layouts, and ensuring outputs are reliable and usable for physical products like labels and packaging. In past projects, I have evaluated and compared generative AI tools for quality, consistency, and production suitability, and documented repeatable processes to make AI outputs scalable and maintainable. I am detail-oriented, comfortable iterating with stakeholders, and focused on delivering results that are ready for real-world deployment rather than experimental demos. I enjoy working at the intersection of AI, design constraints, and practical production needs, and I bring a structured, problem-solving mindset to building AI-powered creative workflows.

Available to hire

I am a Data Science and AI Engineer with hands-on experience designing AI image generation workflows for real-world, production-ready use cases. My work focuses on transforming abstract user inputs into consistent, high-quality visual outputs that meet strict technical constraints rather than just aesthetic goals.

I have practical experience with generative image models and advanced prompt engineering, including designing prompts and validation steps that account for real-world printing requirements such as bleed, trim, and safe zones. I approach AI image generation as a workflow problem defining input constraints, guiding models toward predictable layouts, and ensuring outputs are reliable and usable for physical products like labels and packaging.

In past projects, I have evaluated and compared generative AI tools for quality, consistency, and production suitability, and documented repeatable processes to make AI outputs scalable and maintainable. I am detail-oriented, comfortable iterating with stakeholders, and focused on delivering results that are ready for real-world deployment rather than experimental demos.

I enjoy working at the intersection of AI, design constraints, and practical production needs, and I bring a structured, problem-solving mindset to building AI-powered creative workflows.

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Experience Level

Expert
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Work Experience

Graduate NLP Researcher at Arizona State University - CoRAL Lab
May 1, 2025 - Present
Built query-independent table transformation pipelines in Python, converting unstructured tables into SQL-ready formats and improving QA and analytics accuracy by approximately 40%. Conducted large-scale evaluations on WikiTable Questions, NQ-Tables, HiTab, and SequentialQA, benchmarking Gemini 2.0/2.5, Qwen, LLaMA, Deep Seek, and ChatGPT-OSS, reducing schema-linking and execution errors by 30–35%. Developed a multi-question-per-table challenge dataset to stress-test SQL reasoning, doubling failure coverage compared to standard benchmarks.
Data Analyst Intern at Samisen Distributed Systems Pvt. Ltd
January 1, 2024 - June 30, 2024
Analyzed 5,000+ chatbot interaction records in Python to identify common intent failure cases and support retraining decisions for intent classification models. Refined NLP preprocessing pipelines using spaCy and NLTK, improving intent coverage and increasing correct routing of user queries in pilot evaluations. Produced performance and latency reports using SQL and data visualization tools, helping teams monitor system behavior and reduce response delays by ~10%.

Education

Master of Science in Data Science, Analytics, and Engineering at Arizona State University
January 11, 2030 - May 1, 2026
Bachelor of Technology in Computer Science and Engineering at Vellore Institute of Technology, Vellore, India
August 1, 2020 - May 1, 2024

Qualifications

Microsoft Certified: Azure AI Fundamentals
January 11, 2030 - February 5, 2026
Google Data Analytics Professional Certificate
January 11, 2030 - February 5, 2026
Machine Learning Specialization (Deep Learning.AI)
January 11, 2030 - February 5, 2026

Industry Experience

Software & Internet, Professional Services, Education