K. JATILESH

Conducted end-to-end manual testing for key financial workflows, ensuring compliance with KYC/AML regulations.

Performed API testing to validate currency conversion accuracy and transaction statuses.

Logged and tracked bugs in JIRA, maintaining complete traceability from requirements to deployment.

• Collaborated with cross-functional teams during daily stand-ups and sprint reviews under Agile methodology.
• Executed functional and integration testing of E-NOC request submission and approval workflows.
• Validated user roles and checked end-to-end approval chains for document uploads and signatures.
• Simulated various project submission types for comprehensive workflow testing, including roadworks and building permits.
• Documented bugs in JIRA with detailed reproduction steps and attachments for efficient resolution.

Worked on SPDP for the government. Of Odisha, handling test case design, execution, defect tracking, and client requirement analysis. Participated in review meetings to align QA processes with project goals.
Tested E-Vidyavahini for the Jharkhand government, performing functional, regression, and GUI testing; logged and tracked defects across modules.
For The Gambia’s Social Registry, I analyzed client requirements, executed test cases, set up test environments, and contributed to QA reviews.
Built image detection models using OpenCV, TensorFlow, and Keras with Raspberry Pi; implemented real-time facial recognition, and used MongoDB for data handling.

• TensorFlow Object Detection through Webcams and CCTV with OpenCV.|June 2019 – September 2019, Telangana State Government.
• Efficiently created discriminative models, handled the task of detection via classification, filtered out the object of attention, and objects with similar features. Performed detection through computational methods. Managed activities of dynamic programming.
• Wearable Computing: Human Activity Prediction Using 'Ensemble Learning,' Feb-May 2019.

Played a significant role in the process of data preprocessing and data exploration using R.

Successfully built the human activity prediction model using ensemble learning methods.

Competently utilized R to perform the task of model evaluation and data visualization.

• Track record of implementing and evaluating a simple Convolutional Neural Network for MNIST.
• Managed activities of implementing a close-to-state-of-the-art deep learning model for MNIST.
• Hands-on experience in loading the MNIST dataset in Keras, Python, and deep learning.
• Conducted statistical analysis and developed probabilistic models. Performed the task of network representation and operational research with Python.