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Houman Ekrami
Software Engineer
Professional Summary
Software Engineer with 6+ years of experience and dual master's degrees in Computer Science and UI/UX Design. Proven track record in developing scalable, cloud-native platforms, enterprise tools, and full-stack applications using Python, JavaScript, and Google Cloud. Skilled in REST API design, microservices architecture, and building custom all-in-one test automation platforms. Delivers high-performance, maintainable software that enhances operational efficiency and user experience.
Skills
Languages & Scripting: Python, SQL, JavaScript, PHP, Java, Bash
Frameworks & Libraries: Flask, Bootstrap, React
Cloud & DevOps: Google Cloud Platform, Docker, Kubernetes, Helm, Git
Databases & Storage: PostgreSQL, MySQL/MariaDB, MongoDB, Cassandra, Redis
APIs & Integration: RESTful APIs, Microservices, Load Balancing, RabbitMQ, GraphQL, OAuth
Machine Learning & Data: TensorFlow, PyTorch, scikit-learn, Pandas, NumPy, XGBoost, Swin UNETR
Testing & Workflow: pytest, Postman, Selenium, Agile, Scrum
Work Experience
Software Engineer
September 2019 - Present
Full-time
- Designed and developed Orca, a scalable, cloud-native test infrastructure on Google Cloud for Monta Vista's enterprise Linux distributions based on CentOS, Rocky Linux, and Ubuntu, reducing reliance on physical hardware.
- Accelerated test cycles by over 40% and lowered provisioning and maintenance costs by an estimated 50%.
- Served as the principal contributor to the development of a RESTful CVE Data API, enabling authenticated clients to retrieve package-specific vulnerability metadata and generate custom reports up to 60% faster.
- Led the migration of customer-facing support services from Salesforce to Monta Vista's in-house platform (Zone) for product downloads, patch access, ticketing, and tutorials.
- Improved the customer-facing user experience, enhanced usability for internal support teams, and reduced support system SaaS subscription and maintenance costs by 35%.
- Developed an end-to-end risk analysis and reporting pipeline using machine learning techniques, Python, and PostgreSQL to automate vulnerability tracking across Linux package releases.
- Applied ML to data from multiple sources including package changelogs, external CVE databases, build artifact data, internal Git repositories, Bugzilla, and severity scoring systems to triage and prioritize risk, reducing time spent on vulnerability analysis by up to 90%.
- Designed and built multiple internal tools, including a Document Management System (DMS) compliant with audit and process governance standards, enhancing documentation versioning, traceability, and workflow automation.
- Participated in the candidate interview process for internship positions, contributing to interview process design, talent evaluation, and team growth decisions.
- Managed and maintained a physical test lab of 60+ hardware targets, including ARM, x86, and RISC-V systems, used for Linux distribution validation, ensuring continuous availability and reliability of build and test environments.
Software Developer
October 2017 - September 2018
Internship
- Designed and implemented an event management system with event creation, registration, and participant management features using Apache Usergrid BaaS.
- Built Android app and web interfaces, integrated Elasticsearch and Cassandra NoSQL Database for dynamic event search, and hosted the backend on the Apache Tomcat Web Server.
Education
University of Nevada, Reno
January 2021 - December 2023
Master of Science, Computer Science
Academy of Art University, San Francisco
January 2019 - December 2021
Master of Arts, Web Design and New Media (UI/UX)
Azad University, Tabriz, Iran
January 2014 - December 2018
Bachelor of Science, Software Engineering
Projects
Deep Learning Adrenal-Gland Segmentation Pipeline: Led model selection, training, and radiomic data generation for adrenal segmentation on multi-ethnic chest CT scans, supporting chronic stress biomarker analysis. (Submitted to RSNA 2025) E-CatBoost ICU Mortality Prediction Using ML: Used the MIT EICU Collaborative Research Database to predict ICU mortality. Led data preprocessing, CatBoost training, and comparative model evaluation. (Published in PLOS One, 2022)
Contact
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Houman Ekrami
Software Engineer
Professional Summary
Software Engineer with 6+ years of experience and dual master's degrees in Computer Science and UI/UX Design. Proven track record in developing scalable, cloud-native platforms, enterprise tools, and full-stack applications using Python, JavaScript, and Google Cloud. Skilled in REST API design, microservices architecture, and building custom all-in-one test automation platforms. Delivers high-performance, maintainable software that enhances operational efficiency and user experience.
Skills
Languages & Scripting: Python, SQL, JavaScript, PHP, Java, Bash
Frameworks & Libraries: Flask, Bootstrap, React
Cloud & DevOps: Google Cloud Platform, Docker, Kubernetes, Helm, Git
Databases & Storage: PostgreSQL, MySQL/MariaDB, MongoDB, Cassandra, Redis
APIs & Integration: RESTful APIs, Microservices, Load Balancing, RabbitMQ, GraphQL, OAuth
Machine Learning & Data: TensorFlow, PyTorch, scikit-learn, Pandas, NumPy, XGBoost, Swin UNETR
Testing & Workflow: pytest, Postman, Selenium, Agile, Scrum
Work Experience
Software Engineer
September 2019 - Present
Full-time
- Designed and developed Orca, a scalable, cloud-native test infrastructure on Google Cloud for Monta Vista's enterprise Linux distributions based on CentOS, Rocky Linux, and Ubuntu, reducing reliance on physical hardware.
- Accelerated test cycles by over 40% and lowered provisioning and maintenance costs by an estimated 50%.
- Served as the principal contributor to the development of a RESTful CVE Data API, enabling authenticated clients to retrieve package-specific vulnerability metadata and generate custom reports up to 60% faster.
- Led the migration of customer-facing support services from Salesforce to Monta Vista's in-house platform (Zone) for product downloads, patch access, ticketing, and tutorials.
- Improved the customer-facing user experience, enhanced usability for internal support teams, and reduced support system SaaS subscription and maintenance costs by 35%.
- Developed an end-to-end risk analysis and reporting pipeline using machine learning techniques, Python, and PostgreSQL to automate vulnerability tracking across Linux package releases.
- Applied ML to data from multiple sources including package changelogs, external CVE databases, build artifact data, internal Git repositories, Bugzilla, and severity scoring systems to triage and prioritize risk, reducing time spent on vulnerability analysis by up to 90%.
- Designed and built multiple internal tools, including a Document Management System (DMS) compliant with audit and process governance standards, enhancing documentation versioning, traceability, and workflow automation.
- Participated in the candidate interview process for internship positions, contributing to interview process design, talent evaluation, and team growth decisions.
- Managed and maintained a physical test lab of 60+ hardware targets, including ARM, x86, and RISC-V systems, used for Linux distribution validation, ensuring continuous availability and reliability of build and test environments.
Software Developer
October 2017 - September 2018
Internship
- Designed and implemented an event management system with event creation, registration, and participant management features using Apache Usergrid BaaS.
- Built Android app and web interfaces, integrated Elasticsearch and Cassandra NoSQL Database for dynamic event search, and hosted the backend on the Apache Tomcat Web Server.
Education
University of Nevada, Reno
January 2021 - December 2023
Master of Science, Computer Science
Academy of Art University, San Francisco
January 2019 - December 2021
Master of Arts, Web Design and New Media (UI/UX)
Azad University, Tabriz, Iran
January 2014 - December 2018
Bachelor of Science, Software Engineering
Projects
Deep Learning Adrenal-Gland Segmentation Pipeline: Led model selection, training, and radiomic data generation for adrenal segmentation on multi-ethnic chest CT scans, supporting chronic stress biomarker analysis. (Submitted to RSNA 2025) E-CatBoost ICU Mortality Prediction Using ML: Used the MIT EICU Collaborative Research Database to predict ICU mortality. Led data preprocessing, CatBoost training, and comparative model evaluation. (Published in PLOS One, 2022)
Contact
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