Suloni Praveen
Machine Learning Engineer @Qualtrics
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WORK HISTORY
Machine Learning Engineer @Qualtrics
Provo, UT, US
Design train, and deploy a standalone text classifier to categorize complete vs. incomplete survey responses for the real-time response clarity feature of surveys•Create training pipelines and data preprocessing workflows for response quality assessment•Build complete backend APIs and frontend components to transform response clarity from a post-navigation feature to real-time inline prompting•Enable multiple AI-generated suggestions during active typing while preserving survey author control•Enhance user experience by maintaining question context and respondent motivation through seamless single-page interaction improving survey data quality
EDUCATION
University of Southern California
Masters, Computer Science
Visvesvaraya Technological University
Computer science engineering , Computer Engineering
ABOUT SULONI PRAVEEN
Hi! I’m Suloni Praveen, a Software Engineer focused on building AI systems, scalable backend services, and data platforms that power intelligent applications.I enjoy taking ideas from data and machine learning models to production systems. My work spans ML pipelines, distributed data processing, and backend infrastructure, where I design systems that can handle large volumes of data while remaining reliable, efficient, and scalable.A few highlights from my work:• Built a real-time NLP system at Qualtrics that analyzes survey responses while users type and provides inline prompts, improving response completeness by 40% across 10M+ monthly responses.• Designed a VR telemetry streaming pipeline ingesting 1K+ interaction events per session using Kafka, enabling large-scale behavioral analytics for surgical simulation platforms.• Engineered a distributed ETL pipeline processing 1M+ interaction logs, improving analytics query performance by 45%(Python, Spark, Redis).• Developed scalable ML inference services supporting 50K+ concurrent users, reducing latency by 15% using autoscaling infrastructure and Redis caching.• Built an AI-powered financial analysis platform that converts bank statement PDFs into structured financial insights and anomaly detection signals.• Created an LLM-powered scientific document analyzer using a RAG pipeline, achieving 92% metadata extraction accuracy and reducing literature review time by 70%.I’m particularly interested in working on problems related to:Machine Learning Systems • AI Applications • Distributed Systems • Backend Infrastructure • Data PlatformsCurrently exploring opportunities in Software Engineering, AI Engineering, and Machine Learning Engineering where I can build systems that turn data and models into real-world products.Always happy to connect with engineers, founders, and teams building ambitious technology.
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