AI Systems Engineer specializing in production-grade LLM and multimodal systems. I build large-scale AI-enabled APIs serving 1M+ users, design distributed data pipelines, and develop evaluation platforms that translate experimentation into product decisions.
I am an AI Systems Engineer with a deep focus on bridging the gap between cutting-edge model experimentation and reliable, large-scale production deployments.
Currently at American Express, I drive multiple high-impact AI initiatives. Beyond building GenAI enterprise Search APIs serving 1M+ users and architecting the AGX evaluation platform, I am part of a specialized team developing an intelligent GenAI voice bots initiative designed to transform and elevate customer service experiences.
My expertise covers the full MLOps lifecycle: from deploying distributed data pipelines via Airflow and Spark, to designing production-grade LLM evaluation frameworks and semantic search optimization.
US Serial No. 63/642,980. Developed predictive models for ranking content popularity, improving engagement metrics in adaptive systems.
ROC No. L-110318/2022. Designed an automated ecosystem to efficiently manage user attendance without relying on manual intervention.
Built a PyTorch-based voice-controlled coding interface using LSTM seq2seq targeting visually impaired developers. Integrated StackOverflow API for autonomous error correction and contextual code suggestions.
Multi-model NLP system using BERT masked language modeling to detect and reverse toxic content. Achieved 87% toxicity reduction maintaining text coherence.
Production-ready sentiment analysis system deployed on AWS SageMaker. Built end-to-end ML pipeline representing a complete MLOps workflow.
NLP-based plagiarism detection deploying text similarity algorithms. Deployed as production-ready REST API on AWS SageMaker with 92% detection accuracy.
Thakur, G.
Pathare, A., Mangrulkar, R., Thakur, G., et al.