LFD103: A Beginner's Guide to Linux Kernel Development

Improving Deep Neural Networks: Hyperparameter tuning, Regularization and Optimization

OnBoard Big Data and ML

Python for Data Science and Machine Learning

Neural Networks and Deep Learning : Deep Learning .AI

Structuring Machine Learning Projects : Deep Learning.AI

Convolutional Neural Networks : DeepLearning.AI

Volunteered organising various events under DJ NSS unit including Rally For Rivers,Aarey Tree Plantation Drive ,etc

Student Volunteer

Being a Mentor at The Uplift Project for  Data Structures and Algorithms & Machine Learning / Artificial Intelligence Tech Stacks I did:
◻️ Debugging code & solving doubts of 20+ Mentees for various Leetcode, G4G problems.
◻️ Maintained the repo & handled GIT contributions

Mentor at Uplift Project

Bachelor of Engineering - BE

~ Enabling NETS (NexiGroup) to build effective solutions for the stakeholders in the Digital Payment Lifecycles like Batch Payment Processing, Card Production, Charge Back, Pin Management, SMS Balance modules.
~ Practicing & promoting clean coding techniques.
~ Learning & contributing in design discussions in order to plan the requirement implementation.
~ Migrated xslt to Java Spring Boot code for conversion process optimizing memory consumption from 18GB to just 0.2GB.
~ Reduced the load by upto 60% of scheduled chain integrations on customizing internal activities triggering CRON scheduler.

Senior Software Engineer

~ Devised solutions & worked on JIRA stories (feature requests), tickets/ instances for Payment Solutions at NETS Integration Team in Agile manner using Oracle SOA/OSB suite, Java, SpringBoot, Maven, WebLogic Server, Openshift for SOAP & REST tx.
~ Facilitated integration, middleware File Processing (conversions, refactoring, encryption, archiving) services for batch & online - real time microservices along with documentation & sprint release notes.
~ Deployed the release builds on Jenkins for dev-test environments & worked with SVN VCS, Nexus Artifact Management, git, Bitbucket, OpenShift.

Full Stack Developer (Software Engineer) 

Designed & developed an Event Recommender system for Employees:
~ Collected & processed training data via web scraping Eventbrite, Techgig’s site
~ Model to predict domain type & event type from event titles - NLP Approach (TFIDF)
~ Event - Employee Matching Algorithm (Regex, Pandas, SQL)
~ Learnt & implemented the project following SDLC Guidelines
~ Technologies used: Beautiful Soup, Sklearn, NLTK, Python, Pandas, Pickle

Machine Learning Intern

FAUN is Medium’s one of the largest and most followed independent DevOps publication.  Few of my Medium Tech blogs have been published with the same.

Technical Writer

Published well-researched technical articles on topics like:
◻️ Coding problems for interviews, 
◻️ DBMS for the website's Learning Section.

Technical Content Writing Intern

Cluster Lead

◻️ Implementing optimized industry ready practices in Oracle DB, Java - Core, EE, Servlets, JDBC, Dynamic Web App, Maven Integrated Projects
◻️ Designing SPA using MEAN Stack- Angular & basic web tech- HTML, CSS, JS, jQuery
◻️ Integrating & automating the build process using DevOps concepts & CI tools like Jenkins
◻️ Being trained on Spring, Hibernate.

Full Stack Graduate Engineer Trainee

Youth4work is an platform which empowers youth by providing them tools to assess themselves and showcase their skills. 
◻️ Securing City Wide first ranks in yTests in coding domain, I got selected for the internship.
◻️  During the internship period I successfully posted and updated the content of the portal regularly & answered numerous queries asked by students esp. engineering aspirants in topics relevant to their academics like:
🔹 Programming 
🔹 Creativity.
◻️ The content & responses which I posted were well received by audience & hence resulting in getting positive feedback, reputation points.

Content Editor

~ Speeden up Infra provisioning with custom configurations 4x faster via Migration of Beanstalk workspaces to evented systems of microservices & EKS architecture, following CQRS design pattern with non-relational schema (DynamoDB)
~ Setting up Platform wide Observability by:
Enabling Monitoring (Metrics) via: Prometheus, Grafana & Alerts & driving adoption via svc integrations;
Logging & Tracing for k8s micro-services & lambdas: ADOT layer, Open Telemetry libs, ElasticSearch along with ILM, APM, Jaegar
~ Dabbled around React micro-frontends and DevOps practices - Scripting (init-containers, ssm), security (rbac, auth), networking (ingresses- nginx, istio, r53, iam, security grps) of containerized systems
~ Enhanced Developer Experience via exploring AI tooling, enabling service generator template (Backstage customized) via it's maintenance & updates
~ Enabled multilevel nesting for platform feature permissions via extending RBAC

(NodeJS, Docker, Kubernetes, AWS: ECS, EC2, Lambda, Step

Software Engineer - Platform Team (Developer Experience)

Platform Engg @Peak.ai | prev : Senior Software Engineer @LTI | Commercial AI | FinTech | Full Stack | prev: ML intern

Know about Aastha Mehta's work experience, education, bootcamps, certifications, and much more. Aastha Mehta