Dhruv Raghav
AI/ML Engineer
Phone:
+1-317-551-2981
Email:
Address:
517 drake street, Indianapolis, Indiana, 46202
Date of Birth:
08-02-1997
A Bit About Me
AI/ML Engineer with 6+ years of experience building production-grade machine learning and Generative AI systems that drive real-world impact.
I design scalable AI solutions across financial analytics, risk intelligence, and large-scale data platforms, focusing on end-to-end system development — from data pipelines to deployment and real-time inference.
My work includes building RAG pipelines, multilingual LLM applications, and computer vision systems, integrated with cloud infrastructure and MLOps pipelines to deliver measurable business outcomes.
Currently open to AI/ML Engineer and Generative AI roles in the US.
Work Experience
AI Engineer — Indiana University (USA)
Aug 2024 – May 2025
ML Research Assistant — Indiana University (USA)
Jan 2024 – May 2024
Data Science Research Assistant — Indiana University (USA)
Sep 2023 – May 2024
Machine Learning Engineer — Mphasis (India)
Sep 2020 – Jul 2023
• Built reproducible AI pipelines across 25+ datasets → improved workflow efficiency by 40%
• Applied causal inference & statistical modeling → improved policy insights by 30%
• Automated AI-driven analysis & reporting → increased insight depth by 25%
• Optimized geospatial ML pipelines → reduced preprocessing time by 50%
• Built RAG-based plagiarism detection system using LLaMA-2 → improved detection accuracy by 25%
• Evaluated CodeBERT, GraphCodeBERT, StarCoder → improved classification performance by 40%
• Developed FAISS-based semantic search → increased retrieval precision by 30%
• Fine-tuned multilingual LLaMA-2 → achieved 85% accuracy across 10+ languages
• Optimized models using LoRA & quantization → reduced compute cost by 40%
• Reduced model size by 50% while maintaining performance
• Built credit risk ML models → improved default prediction accuracy by 35%
• Developed fraud detection pipelines → reduced false positives by 25%
• Improved fraud detection precision by 32%
• Reduced fraud exposure by 20% using explainable AI systems
Junior ML Engineer — Persistent Systems (India)
Jun 2019 – Aug 2020
• Built ML pipelines → improved model accuracy by 28%
• Reduced false positives by 22%
• Reduced manual review effort by 30%
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