Prarthana Prathap

• Hands-on experience in installing and configuring switches, routers and Cisco wireless controllers for LAN/WAN.
• Also deployed Spine and Leaf switches and designed networks using the 3-tier HA model.
• Strong knowledge, understanding and experience in core networking TCP/IP, routing protocols (Static, EIGRP and BGP), HSRP, STP, VLAN routing and IP subnetting.
• Automated Network Monitoring and configuration using Python scripts and NetBrain as a proactive measure to ensure uninterrupted network services to the live, production environment.
• Comprehensive knowledge of VoIP phone systems (Avaya), SNMP, Syslog, TACACS, Cisco ISE, NAC, DHCP, DNS, NAT, GRE.
• Created network monitoring dashboards on Splunk, ELK using data from syslogs, custom output data from switches and routers using Python, and custom-polled data using NNMi.
• Knowledge in Linux OS and Linux command line.
• Setup VMs and installed, configured and upgraded NetScout OS for traffic monitoring, and SIP analysis.
• Experience in packet analysis using Wireshark and NetFlow.
• Collaborated with teams across the globe to standardize network monitoring parameters and automating them.
• Understanding of Cisco Firepower and load balancers – NetScaler and ServerIron.
• Brief knowledge of VxLAN (Arista).
• Develop and maintain processes, documentation and metrics for the management, monitoring and capacity planning for network services.
• Been a part of on-call rotation as a level-3 network engineer for troubleshooting and resolving network related incidents for NOC across South East Asia.
• Traffic Engineering Based Failure Recovery in Software Defined Networks Using Machine Learning.

• Traffic engineering based failure recovery in SDN using Machine Learning implemented using Mininet, OpenDay-light / Floodlight Controller.
• Cognizant Technology Solutions, India - Full-stack Enterprise Developer September 2015 July 2017 Worked on creating an end-to-end application, including the database design, backed process and the UI for the application., Implementation of failure recovery mechanism in a software defined network using Machine Learning.
• Several network state measurements are considered to create the data set to train the model.
• REST APIs of the floodlight controller were used to introduce static routes in the switches present in the network.
• Classification and Prediction using real-time data.

Worked on creating an end-to-end application, including the database design, backed process and the UI for the application using Appian, a BPM tool.