LOKESH MEHTA

Software Engineer

India2 yrs experience

Key Highlights

Expert in Java full stack development with proven results.
Significant improvements in system performance and user engagement.
Hands-on experience with cloud-native technologies and CI/CD.

Stackforce AI infers this person is a SaaS developer with strong expertise in cloud-native applications and microservices.

Contact

lokeshmehta325@gmail.com LinkedIn

Skills

Core Skills

JavaSpring BootAngularSqlKubernetesAzureAzure Data Factory

Other Skills

HTMLCSSDockerRESTful APIsTypeScriptMySQLJenkinsPythonBashRetrieval-Augmented Generation (RAG)Docker SwarmMicrosoft AzureAzure DatabricksblobAzure Data Lake

About

As a Specialist Programmer Level 2 at Infosys, I specialize in Java full stack development, leveraging technologies such as Java, Spring Boot, Angular, HTML, CSS, and Bootstrap to build robust and scalable applications. I also have hands-on experience with Docker and Kubernetes, which I use to containerize applications and manage deployments efficiently in cloud-native environments. Previously, during my internship, I worked on creating and maintaining APIs using JavaScript and Node.js, with a focus on MySQL for database management. I enhanced API response times through denormalization and indexing techniques, ensuring optimal performance and seamless integration.

Experience

2 yrs

Total Experience

2 yrs

Average Tenure

2 yrs

Current Experience

Infosys

Software Engineer

Jun 2024 – Present · 2 yrs · Bangalore Urban, Karnataka, India · On-site

Developed and integrated scalable microservices into a client’s enterprise application using Java (Spring Boot),and RESTful APIs, resulting in a 30% improvement in system response time and better customer data accessibility.
Revamped the client’s front-end portal using Angular, TypeScript, HTML, and CSS, leading to a 25% boost in user engagement scores based on post-deployment analytics.
Optimized critical SQL queries and redesigned database indexing for a client handling large-scale transactional data, reducing query latency by 35% and improving reporting speed.
Containerized and deployed over 25 production-grade machine learning models into the client’s Kubernetes cluster (EKS with Rancher) using Docker, enabling scalable inference and reducing downtime incidents by 40%.
Authored and maintained reusable Bash shell scripts to automate build, test, and deployment steps in client CI/CD workflows, improving release reliability and reducing manual errors by 70%.
Implemented and managed client-specific CI/CD pipelines using Jenkins, Docker, and Kubernetes, reducing average deployment time from 1 hour to 12 minutes.
Delivered and deployed cloud-native applications on the client’s Azure Cloud infrastructure, utilizing AKS, Azure Blob Storage, and Data Lake, supporting auto-scaling and secure data access.
Built and automated ETL data pipelines for a retail analytics client using Azure Data Factory, Azure Databricks, and Python, increasing data transformation throughput by 15% and reducing daily job failures.