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Koushik Vasa

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Hauppauge, New York, United States

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Résumé

Jobs verified_user 0% verified
  • Bridge
    AI/ML Engineer
    Bridge
    Aug 2025 - Current (1 year 2 months)
    • Built Bridgette, an agentic AI assistant for a B2B marketplace, replacing a 10+ step workflow (search - filter invite - negotiate)
    with a single conversational interface.
    • Designed a multi-step tool-calling agent using Claude (Anthropic API) and DSPy for structured prompt optimization and execution
    control.
    • Designed agent decision framework enabling autonomous execution across retrieval, structured queries, and response generation.
    • Architected production dual-agent system (companies vs. experts) supporting workflows such as expert matching, engagement
    creation, and earnings analysis via natural language.
    • Developed 8 production-grade tool schemas with strict runtime validation (Zod), ensuring relia
  • Capgemini
    AI Engineer
    Capgemini
    Apr 2021 - Dec 2023 (2 years 9 months)
    • Built document intelligence platform using hybrid RAG (Azure OpenAI + BM25), replacing manual document review for ~ 120
    internal users across multiple teams.
    • Improved retrieval accuracy for enterprise document queries by ~32% by combining dense embeddings with BM25 re-ranking
    across 500K+ unstructured documents.
    • Designed LangGraph-based pipelines with clear separation of retrieval, reasoning, and response generation, improving response
    reliability by ~35%, reducing incomplete and inconsistent outputs in production.
    • Developed FastAPI inference services handling 200+ concurrent requests with consistent sub-second response times.
    • Introduced confidence-based validation and context filtering that redu
  • C
    Machine Learning Engineer
    CitiusTech Healthcare Technology Pvt. Ltd.
    Aug 2019 - Mar 2021 (1 year 8 months)
    • Built Python/SQL pipelines processing 1M+ patient and claims records, reducing data preparation time by ~30% for downstream
    ML workflows.
    • Developed 30-day hospital readmission prediction models using XGBoost and Random Forest, achieving ~82% AUC and enabling
    early identification of high-risk patients.
    • Applied PCA and domain-driven feature engineering on clinical and claims data, improving model F1-score by ~12% and reducing
    feature dimensionality by ~40%.
    • Resolved recurring pipeline failures including data inconsistencies and job timeouts, reducing failure rates by ~35% while
    consistently meeting SLA timelines.
Education verified_user 0% verified
  • GEORGE MASON UNIVERSITY
    Master of Science in Computer Science (Machine Learning)
    GEORGE MASON UNIVERSITY
    Jan 2024 - Dec 2025 (2 years)
    GPA 3.87
Projects (professional or personal) verified_user 0% verified
  • NA
    MediConnect: AI-Powered Healthcare Matching Platform
    NA
    Jan 2025 - Current (1 year 9 months)
    • Built and deployed AI-powered patient-to-doctor matching platform using 2.8M+ CMS clinician records, enabling real-time
    symptom-based specialist discovery
    • Designed Gemini 2.5 Flash-based recommendation engine with confidence scoring and fallback handling for ambiguous inputs
    • Developed multi-factor compatibility scoring system ranking doctors based on symptoms, geolocation, and predicted consultation
    cost
    • Built conversational AI assistant supporting structured intake, image uploads, and contextual diagnostic guidance
    • Implemented Node.js + Express APIs (5 endpoints) powering search, ranking, and recommendation workflows
    • Designed interactive frontend with OpenStreetMap-based distance calculations
  • NA
    CitationSleuth: RAG-Based Fact Verification System
    NA
    Jan 2024 - Current (2 years 9 months)
    • Built dual-layer LLM validation system combining semantic retrieval and Neo4j graph traversal to verify generated claims
    • Improved verification precision by linking embedding-based evidence retrieval with graph-based relationship validation
    • Developed real-time interface surfacing low-confidence or unsupported outputs before downstream usage
  • NA
    ClearCare: AI-Powered Clinical Data Pipeline
    NA
    Jan 2023 - Current (3 years 9 months)
    • Built end-to-end pipeline ingesting structured EHR and unstructured clinical notes, normalizing data for downstream ML and
    analytics workflows
    • Automated validation and transformation in Python, reducing manual preprocessing effort and recurring data quality issues
    • Integrated LLM-based entity extraction to standardize clinical terminology, improving consistency of training datasets