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I am an AI engineer with experience building production-ready generative AI systems that move from raw user input to reliable, real-world outputs. My work spans prompt engineering, workflow design, and evaluation of AI models for consistency and quality, with a strong focus on practical constraints rather than demos. I have built and deployed AI systems for content generation, recommendation, and decision support, combining careful prompt design with validation and iteration. What sets me apart is my attention to detail and my ability to translate abstract requirements into repeatable, high-quality AI workflows that are ready for real use, not just experimentation.…I am an AI engineer with experience building production-ready generative AI systems that move from raw user input to reliable, real-world outputs. My work spans prompt engineering, workflow design, and evaluation of AI models for consistency and quality, with a strong focus on practical constraints rather than demos. I have built and deployed AI systems for content generation, recommendation, and decision support, combining careful prompt design with validation and iteration. What sets me apart is my attention to detail and my ability to translate abstract requirements into repeatable, high-quality AI workflows that are ready for real use, not just experimentation.

Nilaa Raghunathan

Data Scientist, Data Analyst, Full Stack Developer, +2

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I am an AI engineer with experience building production-ready generative AI systems that move from raw user input to reliable, real-world outputs. My work spans prompt engineering, workflow design, and evaluation of AI models for consistency and quality, with a strong focus on practical constraints rather than demos. I have built and deployed AI systems for content generation, recommendation, and decision support, combining careful prompt design with validation and iteration. What sets me apart is my attention to detail and my ability to translate abstract requirements into repeatable, high-quality AI workflows that are ready for real use, not just experimentation.…I am an AI engineer with experience building production-ready generative AI systems that move from raw user input to reliable, real-world outputs. My work spans prompt engineering, workflow design, and evaluation of AI models for consistency and quality, with a strong focus on practical constraints rather than demos. I have built and deployed AI systems for content generation, recommendation, and decision support, combining careful prompt design with validation and iteration. What sets me apart is my attention to detail and my ability to translate abstract requirements into repeatable, high-quality AI workflows that are ready for real use, not just experimentation.

Available to hire

I am an AI engineer with experience building production-ready generative AI systems that move from raw user input to reliable, real-world outputs. My work spans prompt engineering, workflow design, and evaluation of AI models for consistency and quality, with a strong focus on practical constraints rather than demos. I have built and deployed AI systems for content generation, recommendation, and decision support, combining careful prompt design with validation and iteration. What sets me apart is my attention to detail and my ability to translate abstract requirements into repeatable, high-quality AI workflows that are ready for real use, not just experimentation.

Skills

Experience Level

Expert

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Expert

Work Experience

Machine Learning Intern at Limex Trading

September 1, 2025 - December 1, 2025

Led the design and validation of a portfolio risk forecasting framework using factor-based and covariance models (PFA, GARCH variants, DCC, Ledoit-Wolf) across 102 assets, boosting forecasting accuracy by 29%. Implemented rolling-window experiments and backtesting across 71 model configurations to detect under-forecasting in low-volatility regimes (78%). Established a Monte Carlo-based evaluation framework to validate tail risk (VFR=1.08) in high-volatility regimes.

Data Science Intern at Magna International Inc.

June 1, 2025 - August 1, 2025

Enhanced an ML-based inventory demand forecasting system, reducing safety stock by 12% and saving $1.4M annually. Increased forecast accuracy by 18% (MAPE) using probabilistic time-series modeling (GMM-HMM). Built a context-aware NLP pipeline (BiLSTM-RoBERTa) over 7K+ supplier records. Lowered stock-out risk by 13% via ANOVA, A/B testing & causal impact analysis. Delivered a Streamlit dashboard on Databricks with a MySQL backend to support Monte Carlo simulations for cross-functional teams.

Applied Research Intern at Vellore Institute of Technology

April 1, 2022 - July 1, 2023

Published first-author IEEE Access paper on sentiment analysis deployment, identifying multilingual, cross-domain, and small-scale failure modes across transformer models to establish rigorous ML evaluation practices. Evaluated mitigation strategies (transfer learning, multilingual embeddings, modality fusion) to improve scalability and reliability.