Azadeh Samadian

- Utilized Python's multi-threading for automation, optimizing performance by managing concurrent tasks efficiently. - Designed a ChatBot APP to interact with SQL Databases using natural language and SQL LLM agents - Developed comprehensive testing strategies to validate database performance, encompassing latency, throughput, and scalability metrics across MongoDB, Redis, and DynamoDB, specifically testing on Verizon 5G network conditions. Utilized Jupyter Notebook for detailed analysis of machine learning algorithms, enabling informed decision-making and performance optimization in database operations. - Led the design and implementation of end-to-end automation for network device testing (Cisco, Ciena, Juniper) using Python, Ansible, and Jenkins. Took ownership of creating scripts to establish device connections, execute network tests, and automate reporting via JIRA, optimizing the deployment and testing workflows. Ensured consistency and reliability in network performance validation across diverse environments, driving operational efficiency. Consultant through Photon Solutions LLC

- Designed and implemented a Kubernetes-based microservices architecture using Amazon EKS (Elastic Kubernetes Service) and Amazon EC2 instances. Orchestrated containerized applications across EKS clusters for scalability and fault tolerance. Configured EC2 instances to optimize performance and resource allocation. Implemented CI/CD pipelines with tools like Jenkins for automated deployments. Ensured high availability and security using AWS IAM and VPC configurations. - Developed scalable data analytics pipeline on AWS infrastructure. Utilized Amazon EC2 for compute resources, Amazon S3 for data storage, and Amazon VPC for network isolation. Implemented ETL processes to ingest and transform data using AWS Glue and Apache Spark. Integrated Splunk for real- time data monitoring and analysis, and Grafana for visualizing key performance indicators. Enhanced system reliability and performance through automated scaling and monitoring using AWS CloudWatch and Prometheus. - Led a project focused on modernizing legacy mainframe systems to Java-based front-end and backend architectures. Designed and implemented robust Java applications to replace legacy COBOL and RPG. Migrated data and business logic to modern database systems and microservices architecture, enhancing scalability and performance. Implemented RESTful APIs for seamless integration with existing systems and improved user interfaces. Ensured compliance with industry standards and best practices throughout the modernization process. -Experience in designing and developing distributed systems in Microservices architecture, JAVA 8+, REST API, Spring Boot, Angular, Oracle, maven, Docker, Kubernetes, CI/CD pipeline, SQL/NOSQL

- Developed robust big data platform on AWS to handle large-scale data processing and analytics tasks. Designed and implemented data pipelines using Apache Spark on Amazon EMR for batch processing and Apache Kafka for real-time streaming. Integrated data from various sources into Amazon S3 for efficient storage and retrieval. Implemented ETL processes using Apache Airflow to orchestrate data workflows and ensure data quality and consistency. Leveraged AWS Lambda for serverless computing to enhance scalability and reduce operational overhead. Employed AWS IAM and VPC to enforce security best practices and ensure data confidentiality and integrity throughout the platform. - Worked on developing plugin and APIs for Atlassian Jira and Confluence using Java and Groovy

- Worked with big data services like Kafka, Spark Streaming, Zookeeper, HBase, HDFS, Solr - Worked with big data services like Kafka, Spark Streaming, Zookeeper, HBase, Solr, Splunk, Grafana - Implemented an integrated monitoring and visualization system using Splunk for log management and Grafana for real-time dashboards. Enhanced system observability by combining log data from Splunk with real-time metrics in Grafana. Improved operational efficiency with unified monitoring dashboards and alerting. (Splunk , Grafana, Prometheus, AWS EC2) - Developed an automated data pipeline for real-time analytics using a Python script to run daily queries on Apache Solr. Processed and ingested data into MySQL, optimizing performance and integrity. Visualized metrics with Grafana, creating interactive dashboards and enhancing system reliability. - Developed a scalable data processing system using AWS services and Apache Kafka. Implemented real-time data pipelines with AWS Lambda, Amazon S3, and Amazon RDS. Deployed microservices with Docker and Kubernetes for high availability and fault tolerance. Monitored performance using AWS CloudWatch and Grafana, ensuring efficient resource utilization and scalability. - Statistical analysis on violation databases: I implemented Python script to run queries on Solr daily and ingested the output to Mysql and used Machine learning algorithms on dataset- Worked with big data services like Kafka, Spark Streaming, Zookeeper, HBase, HDFS, Solr - Statistical analysis on violation databases: I implemented Python script to run queries on Solr daily and ingested the output to Mysql and used Machine learning algorithms on dataset

- Engineered microservices in Golang, orchestrating deployment through Docker and Kubernetes for seamless scalability and high availability. Implemented rigorous unit testing and test vectors across development and production environments, ensuring robust application reliability. Established CI/CD pipelines to streamline continuous integration and delivery processes. Designed efficient REST APIs and utilized Go routines to optimize application performance and responsiveness. - Implemented ETL processing with PySpark on AWS EMR, enabling efficient handling of large-scale data. Conducted ML statistical analysis in Jupyter Notebook, leveraging advanced algorithms to derive actionable insights. - Implemented Microservices in Golang: I Implemented different location engine Microservices in Golang using Docker, Redis, etc, the code was originally in Matlab. I implemented unit tests and test vectors for them and eventually tested them in development and production - Statistical analysis comparison for databases using Spark and Jupyter notebook: I created Spark apps for generating low-level statistics and ran them on AWS EMR. Then I used rolled up data in Jupyter Notebook for high-level quantitative analysis. I used different machine learning algorithms on them - Third party data analysis: I worked with big data from some third party companies and ran analysis on them to improve our database. I used Spark, MongoDB, Jupyter notebook

- Worked on projects involving Machine Learning algorithms including decision trees, classification, logistic regression, recommender systems, and text analytics using Python. Utilized toolsets such as Scikit-learn, NumPy, and Pandas to extract meaningful insights from data and drive actionable results. Content Level Deduplication of News Articles using Apache Kafka -I used news-please API to extract data and Textrank algorithm to produce top k keywords and set a threshold to filter the potential duplicates, then I used Topic Modeling and stored the duplicate in the Mango DB. Statistical Analysis on a BlockChain Token -I developed a pipeline to preprocess a BlockChain token (Network) and detected the underlying distribution of the data by using R Studio. I splitted the data to several layers and extracted features to find a regression model to predict the future price. Path Computation Element (PCE) Simulator -An event driven simulator in C++ used for estimating light path OSNR (Optical Signal to Noise Ratio) and assigning the best modulation scheme and routing and spectrum assignment based on the given request. This will result in higher transmission rate and lower number of transponder and better spectrum efficiency in optical networks. Collaborative Filtering on Netflix Ratings -I implemented the memory based collaborative filtering by using cosine similarity measurement.

- Implemented a Software-Defined Networking (SDN) project using C++ with Dense Wavelength Di- vision Multiplexing (DWDM) technology. Automated network configuration and optimized bandwidth allocation across wavelengths to improve data center performance and scalability. - Proficient in a wide range of network protocols including OSPF, BGP, TCP/IP, UDP, DNS, SNMP, and VLANs. Experienced in configuring and troubleshooting network protocols to ensure optimal performance and reliability in diverse network environments. - Worked on Software-Defined Networking (SDN) in C++ - Network protocols OSPF, BGP, TCP, UDP, DNS, etc - optical engineer –design network according to customer requirement with technologies like : MSAN, GPON,XGPON, Metro-E, IP RAN, IP MPLS, MPLS-TP, PTN, SDH, DWDM, OTN, POTN,... - GITEX 2014 exhibitor