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Laasya Muktevi - Machine Learning Intern in San Jose, CA

Laasya Muktevi

Intern Machine Learning Engineer specializing in forecasting, NLP, and RAG systems

Featurebox AICalifornia State University, Long BeachSan Jose, CA5 Years ExperienceIntern LevelWorks On-Site

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About

Intern who built and deployed a production LLM-powered contract analysis system for finance teams: Azure Document Intelligence for text/table extraction plus Gemini prompting to surface key terms and risks via an async API and simple UI. Emphasizes reliability in production with fallbacks, guardrails against hallucinations, and operational concerns like latency/cost/versioning, delivering summaries in under 30 seconds instead of hours.

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Key Strengths

  • Built and deployed LLM-powered contract analysis workflow end-to-end (extraction → LLM summarization → API → UI)
  • Improved contract review turnaround from hours to under 30 seconds
  • Handled messy PDF extraction with confidence thresholds, fallbacks, retries, and batching
  • Designed prompt/workflow guardrails to reduce hallucinations and stabilize outputs across contract styles
  • Production-minded deployment focus (latency, cost, and versioning for long documents)
  • Strong orchestration experience using Airflow (DAGs, sensors, retries, SLA monitoring) for reliable ML/data pipelines
  • Reliability-focused evaluation approach using ground-truth datasets, stress tests, and step-level validation

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Experience

Machine Learning InternFeature Box AI · Aug 2025 – Presentinternship
Machine Learning Research InternCalifornia State University, Long Beach · Sep 2023 – May 2025internship
Machine Learning EngineerDeloitte Consulting · Oct 2020 – Aug 2023

Education

California State University, Long Beachmaster, Information Systems (2025)

Skills

A/B TestingAgileAmazon EC2Amazon S3Anomaly DetectionApache SparkArtificial IntelligenceAWSBusiness IntelligenceC++Data AnalyticsData CleaningData EngineeringData PipelinesData Structures

Languages

English

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Connect with Laasya

Laasya already has a relationship with Reval, so a warm intro from us gets a much better response than cold outreach.

Typically responds within 24 hours

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Laasya MukteviIntern Machine Learning Engineer specializing in forecasting, NLP, and RAG systems