About Me

  • Full Name:Dhwanit Gajjar
  • Email:dhwanitgajjar@gmail.com
  • Address:Edison, New Jersey

Hello There!

As a Computer Science graduate from Rutgers University, I am currently working as an AI/ML Associate at JPMorgan Chase in Houston, where I develop cutting-edge AI solutions for legal operations. My expertise spans from building NDA evaluation systems using GPT models and BERT-based case recommendation systems, to implementing RAG-based knowledge management solutions. With a proven track record of deploying production ML models on AWS (including previous work at MetLife enhancing claims processing efficiency by 32% and at Syneos Health achieving 92% accuracy in patient outcome predictions), I excel at translating complex data into actionable business insights. Proficient in Python, SQL, cloud platforms, and modern MLOps practices including CI/CD pipelines with Jenkins, Terraform, and Kubernetes, I am passionate about leveraging AI to solve real-world challenges in enterprise environments.

Currently specializing in Legal AI solutions at JPMorgan Chase

My Resume

  • Work Experience

  • AI/ML Associate

    JPMorgan Chase, Texas (Aug 25' - Present)
    • Designed and implemented an NDA Evaluation App for the Legal team, leveraging GPT models to analyze NDAs against a rulebook and recommend revisions, significantly improving contract review efficiency.
    • Maintained and enhanced the FITS case recommendation system, applying BERT-based NLP models to analyze historical legal outcomes and advise whether to litigate or settle, reducing decision-making time.
    • Built a Knowledge Management LLM (RAG-based) solution to store institutional legal knowledge, enabling natural-language query answering and streamlining legal research.
    • Engineered end-to-end CI/CD pipelines using Jenkins (Jules wrapper), Terraform, and Spinnaker, deploying production workloads on AWS Fargate and EKS; managed storage in S3, secrets in AWS Secrets Manager, and deployments via kubectl, improving scalability, security, and delivery speed.
    • Collaborated in an Agile environment using JIRA for sprint planning and Confluence for documentation, ensuring transparency and alignment across cross-functional teams.

  • Machine Learning Engineer

    MetLife (April 24' - Aug 25')
    • Collaborated with a team of 7 to develop business solutions, reducing the debugging time by 72% for recurring tasks.
    • Developed and deployed 5+ TensorFlow-based predictive models on AWS SageMaker, enhancing claims processing efficiency by 32% and reducing manual review time by 40%.
    • Developed a data validation script using Python (Pandas, NumPy) and SQL, ensuring 99.5% data accuracy and reducing data inconsistencies by 30%.
    • Designed and deployed a claims prediction model using Gradient Boosting and LSTM networks, integrated with Apache Kafka for real-time data ingestion, resulting in 35% reduction in claims processing time and annual cost savings by 7-figure.
    • Implemented a data pipeline using Apache Kafka, Apache Spark, and Python, increasing data freshness by 75% and supporting timely business decisions.
    • Integrated Amazon Lex bots with AWS services like Lambda, DynamoDB, S3, and API Gateway, enabling dynamic and scalable backend operations.
    • Utilized Docker, Jenkins, and Kubernetes to automate ML model deployment, reducing deployment time by 30%.
    • Conducted A/B testing and hyperparameter tuning using Scikit-learn, resulting in a 25% improvement in model accuracy.

  • Junior Data Scientist, Machine Learning

    Syneos Health (Oct 21’ – Mar 23’)
    • Built and deployed 3+ Keras-based Deep Learning models for patient outcome prediction, achieving a 92% accuracy rate and informing enhanced treatment planning.
    • Developed data visualizations using Tableau and Power BI, presenting actionable insights to non-technical stakeholders and driving data-driven decision-making.
    • Automated data processing tasks using Python, Bash, and SQL, reducing manual effort by 60% and increasing team productivity.
    • Built a scalable NLP application prototype on AWS utilizing services like Lambda, DynamoDB, and AppSync.
    • Implemented a data pre-processing workflow for ML systems, resulting in a 20% improvement in data accuracy and consistency, and a 30% increase in predictive performance.
    • Designed a scalable data warehouse solution using AWS Redshift that reduced query response time by 60% and increased data retrieval efficiency by 40%.
    • Developed a data warehousing solution using PostgreSQL, SQLAlchemy ORM, and Apache Spark, supporting 500+ concurrent users and reducing query latency by 80%.
    • Applied statistical process control (SPC) to monitor clinical trial data, detecting outliers within 2 hours of occurrence and ensuring data quality.

  • Education

  • Bachelors Degree

    Computer Science

    Rutgers State University, New Brunswick (2021 - 2025)

  • Associates Degree

    Computer Information Systems

    Middlesex College (2021 - 2023)

  • Certificates

  • Solutions Architect - Assoiciate

    Amazon Web Services

  • Cloud Practioner

    Amazon Web Services

Projects

Pizzeria App

Android and a PC pizza ordering app

Sales Forecasting

Real-time forecasting system using ARIMA, with Flask dashboards for dynamic visualization.

RU Clinic

Java based clinic management system

Anime Recommendation

Deep Learning-based Anime Recommendation System that leverages collaborative filtering to provide personalized anime suggestions based on user input and preferences.

Want to work Together?

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