Samhitha Medulla

Experience

• 

  SmartBridge

  Oct 2019 - Dec 2019

  IoT Intern

  • Developed and deployed chatbots using IBM Watson, improving user engagement and automating responses for various applications, resulting in a 40% increase in user interaction and a 30% reduction in manual customer support tasks.• Designed and implemented a Smart Home Automation system that controls home appliances, including remote management of lights, utilizing IoT devices, IFTTT, and Google Assistant for seamless voice commands, enhancing user convenience and energy efficiency by 25%.• Integrated smart home devices with cloud-based services to ensure real-time control and monitoring, boosting system reliability and user satisfaction.• Optimized automation workflows by integrating custom routines, allowing users to set schedules and preferences for home appliances, improving overall system responsiveness by 20%.• Collaborated with cross-functional teams to refine chatbot algorithms and enhance voice recognition capabilities, delivering an intuitive, responsive user experience across multiple platforms.• Utilized IBM Watson, IoT devices, Google Assistant, and IFTTT to build scalable automation systems, improving both user experience and operational efficiency. Show less

• 

  Ingram Micro

  Jul 2021 - Dec 2021

  Associate Engineer Information Security

  • Monitored security events using SIEM tools to detect and analyze potential threats, initiating incident response protocols and escalation procedures, reducing response time by 30% and improving system security.• Performed comprehensive log analysis from multiple sources, identifying anomalous behavior and correlating data to improve threat detection, resulting in a 25% increase in detection accuracy and reducing false positives by 20%.• Developed automated workflows and alerts to prioritize high-risk security incidents, enhancing threat mitigation efforts and improving overall incident management efficiency by 35%.• Collaborated with cross-functional teams to optimize incident response strategies, ensuring timely escalation and resolution of critical security events, minimizing downtime by 15%.• Implemented advanced correlation techniques to identify emerging security trends, proactively strengthening defenses and decreasing the risk of security breaches by 20%.• Utilized SIEM tools, log analysis, and incident response protocols to enhance overall security posture, supporting organizational risk management and compliance efforts. Show less

• 

  Ivy

  Dec 2021 - Jul 2023

  Project: Event Tracking & Data Processing Pipeline for Real-time Analytics Using Azure DatabricksDuration: Dec 2022 – July 2023Tools & Technologies: Azure Databricks, Delta Lake, Azure Event Hubs, PySpark, MLflow, Hugging Face Transformers, Power BI, Apache Kafka, Azure Blob Storage, PythonOverview:Led the design and development of a real-time sentiment analysis pipeline to monitor and analyze Twitter (X) data streams using Azure Databricks, Apache Kafka, and Azure Event Hubs, applying AI/ML models for tweet classification and delivering live insights through Power BI dashboards.Key Responsibilities:• Developed a complete real-time data engineering pipeline using Azure Event Hubs for ingestion, Databricks Auto Loader for streaming input, and Delta Lake to maintain raw, processed, and enriched tweet data.• Architected a multi-layer Delta Lake system (Bronze → Silver → Gold) to implement data transformation, quality checks, and sentiment enrichment.•Implemented PySpark transformations in Databricks notebooks to clean and normalize tweet text (removing hashtags, links, emojis, and stop words).• Integrated a Hugging Face Transformers-based sentiment model to classify tweets into Positive, Negative, and Neutral categories using Databricks MLflow and inference pipelines.• Created a Gold layer output table containing cleaned text, sentiment scores, and classification labels for downstream analytics.• Scheduled and orchestrated the entire pipeline using Databricks Jobs, enabling automated runs for ingestion, transformation, sentiment scoring, and output updates without manual intervention.• Designed and deployed a Power BI dashboard using Gold table data, offering live visualizations of sentiment trends, word clouds, KPIs, and time-series analysis for real-time insights.• Simulated a customer events stream using a Python-based tweet generator to mimic API data flow for testing and demonstration purposes. Show less Project: AppsFlyer Hourly Data Processing System • Automated data consumption from the AppsFlyer Hourly S3 bucket, streamlining data preparation and secure transfer for BI analysis, reducing data handling time by 40%.• Sourced two main types of data—app installs and in-app events—directly from AppsFlyer for downstream processing, improving data collection accuracy by 30%.• Initiated the data pipeline every hour using a Cron Job, fetching lists from specific S3 folders for installs and in-app events, ensuring timely data availability for BI analysis.• Prepared data by adjusting attributes and updating filenames to meet required specifications, ensuring consistency and reducing manual data processing by 50%.• Transferred processed data securely to an SFTP server using the putSFTP command, enabling BI team access for analysis and reporting with 100% data integrity.• Enabled BI team members to receive processed data via email or access it directly from the server, enhancing data accessibility and supporting timely decision-making.• Utilized AppsFlyer, AWS S3, SFTP, Cron Job, and automation scripting in Python and Shell to ensure efficient data flow, boosting team productivity and data accessibility. Show less

  • Software Engineer

    Dec 2022 - Jul 2023

  • Junior Software Engineer

    Dec 2021 - Dec 2022