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Technical Lead• Directed a team of four developers to build a fully containerized, integrated knowledge graph application that reduced root problem diagnosis time by 45%, leveraging Docker, OrientDB, Elasticsearch, Node.js, and React.• Led a team of seven developers to create a fully containerized Installation Verification Tool, reducingdeployment time by 20%, decreasing client reversal by 15%, and enhancing customer installationexperience, using Docker, Bash, and Python.Software Engineer, Band 7 • Developed a message rarity feature using Python's machine learning libraries, decreasing false positiveanomalies detection by 61% and achieving a 35% reduction in root problem diagnosis >me.• Modified product data architecture from indexes to data streams via Elastic stack, Opensearch, and Python,increasing shard efficiency and search times by 30%.• Automated product installation and deployment for an innovation project using Ansible and Jenkins,reducing install and deploy time by 50% for developers and testers.• Enhanced Opensearch’s alerting plugin to enable alerting capabilities on cloud based applications through Kotlin and JavaScript.• Automated vulnerability scanning in source control by integrating Jenkins with container and static scanprocesses.• Containerized python based applications into services using Docker reducing setup time by 30% andimproving overall user satisfaction with the deployment processSoftware Engineer, Band 6 • Built an end-to-end demo environment and app with Python and Flask to demonstrate the product’sanomaly detection and prediction capabilities.• Developed backend infrastructure for IBM’s workload visualizer using Node.js, enhancing visualizationcapabilities for complex workloads.

• Worked with agile development • Created a full stack SIEM app that extends and accommodate alternative data analysis tools • Worked with Splunk’s Machine Learning capabilities to analyze and visualize zMainframe data• Created advanced Splunk Apps to run custom scripts• Managed continuous integration and deployment with Travis CI and Docker.

• Used Agile methodologies• Engineered algorithms to increase query speeds of big data• Used Scala, Hive, and Spark to analyze data sets• Received files from TOPs Installation Reporting tool and removed unnecessary columns from raw files • Created table extracts to change the live connection from hive to tableau (data visualization tool) to a static connection• Removing data from raw files and creating extracts increased query speeds by over 160%.

• Wrote queries to retrieve data from Elastic Search, then developed and implemented Machine Learning algorithms that increased the speed of statistic calculations by 200%. • Assisted the Cyber Security department and wrote events in Python that detected suspicious email activity.