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I’m Sahil, an MS Data Science candidate (University at Buffalo) who builds production-grade GenAI workflows in Python. I specialize in prompt engineering, repeatable generation pipelines, and automated QA so outputs are consistent and usable, not just “cool demos.” For this project, I’d design a spec-driven image generation workflow that turns user notes and label dimensions into print-ready backgrounds (bleed/trim/safe zones, aspect and resolution constraints), using Google AI Studio/Imagen 4 (or the best available alternative), with clear documentation and iteration loops to reach stable, high-quality results.…I’m Sahil, an MS Data Science candidate (University at Buffalo) who builds production-grade GenAI workflows in Python. I specialize in prompt engineering, repeatable generation pipelines, and automated QA so outputs are consistent and usable, not just “cool demos.” For this project, I’d design a spec-driven image generation workflow that turns user notes and label dimensions into print-ready backgrounds (bleed/trim/safe zones, aspect and resolution constraints), using Google AI Studio/Imagen 4 (or the best available alternative), with clear documentation and iteration loops to reach stable, high-quality results.

SAHIL SUBHASHBHAI VACHHANI

Data Scientist, AI Developer, AI Engineer, +5

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I’m Sahil, an MS Data Science candidate (University at Buffalo) who builds production-grade GenAI workflows in Python. I specialize in prompt engineering, repeatable generation pipelines, and automated QA so outputs are consistent and usable, not just “cool demos.” For this project, I’d design a spec-driven image generation workflow that turns user notes and label dimensions into print-ready backgrounds (bleed/trim/safe zones, aspect and resolution constraints), using Google AI Studio/Imagen 4 (or the best available alternative), with clear documentation and iteration loops to reach stable, high-quality results.…I’m Sahil, an MS Data Science candidate (University at Buffalo) who builds production-grade GenAI workflows in Python. I specialize in prompt engineering, repeatable generation pipelines, and automated QA so outputs are consistent and usable, not just “cool demos.” For this project, I’d design a spec-driven image generation workflow that turns user notes and label dimensions into print-ready backgrounds (bleed/trim/safe zones, aspect and resolution constraints), using Google AI Studio/Imagen 4 (or the best available alternative), with clear documentation and iteration loops to reach stable, high-quality results.

Available to hire

I’m Sahil, an MS Data Science candidate (University at Buffalo) who builds production-grade GenAI workflows in Python. I specialize in prompt engineering, repeatable generation pipelines, and automated QA so outputs are consistent and usable, not just “cool demos.” For this project, I’d design a spec-driven image generation workflow that turns user notes and label dimensions into print-ready backgrounds (bleed/trim/safe zones, aspect and resolution constraints), using Google AI Studio/Imagen 4 (or the best available alternative), with clear documentation and iteration loops to reach stable, high-quality results.

Skills

Experience Level

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Expert

Work Experience

Applied NLP & Data Science Intern at Media Sales Plus Inc.

August 1, 2025 - December 1, 2025

Applied NLP techniques for text validation, content structuring, and workflow optimization in product features. Implemented LLM-driven pipelines including prompt design and evaluation to enhance model reliability. Conducted Python-based data analysis and supported backend/API integrations to ensure end-to-end functionality.

Machine Learning and Deep Learning AI Intern at Dinjan Infotech Pvt. Ltd.

January 1, 2024 - April 1, 2024

Built ML solutions for credit card approvals, breast cancer detection, and Uber trips analytics, achieving 90-95% accuracy. Deployed Telegram + LLM chatbots (OpenAI/Gemini) to automate insights and query handling, cutting manual analysis time 35%. Built web-scraping + NLP pipeline to collect 10K records and streamline text analysis/reporting.