Problem Solving

Problem Solving (Advanced)

Getting started with Deep Learning

GFG Job-a-thon 10 hiring challenge (983 rank)

Python

Java

Meta Front-End Developer

Data Structures and Algorithms

Master of Science - MS

Bachelor of Technology - BTech

◆ Migrated 9 Spring Boot APIs from managing configurations in AWS RDS to AWS AppConfig, enhancing security by 70% through IAM and reducing infrastructure costs by $27/week. ◆ Built centralized non-blocking logging in Python FastAPI for AI services using LangChain and Amazon Bedrock, reducing latency by 250ms/request and logging effort by 60%, enabling better customer usage insights. ◆ Centralized error handling across 43 Temporal workflows in tenant registry services, reducing retry logic by ∼ 98% and enhancing maintainability of long-running Spring Boot services.

Software Engineer Intern - ( AI )

◆ Led the migration of core micro-frontend from React v14 to v18, fixing legacy bugs, preventing regressions in prod.

◆ Optimized application architecture with Redux, and load distribution, cutting bundle size from 300MB to 3MB using Webpacks and reducing load time from 12s to 3.5s.

◆ Boosted UI responsiveness 400% with infinite scroll, lazy loading, delivering components 5× faster using Material-UI.

◆ Developed mock APIs in Node.js for E2E testing, enabling faster prototyping and reducing QA cycles by 30%.

Frontend Engineer Intern

◆ Fixed 3 functional bugs while migrating ”RabbitLoader” to React.js 18, avoiding breaking changes in production. ◆ Optimized application architecture with micro-frontends, Redux, and load distribution, cutting bundle size from 300MB to 3MB using Webpacks and reducing load time from 12s to 3.5s. ◆ Enhanced UI responsiveness by 400% through pagination, lazy loading, and infinite scrolling, accelerating component delivery 5x with Material-UI. ◆ Built server-side mock APIs with Express.js for integration testing and streamlined feature prototyping.

Frontend Developer Intern

◆ Built and deployed 12 Python AWS Lambdas using API Gateway, leveraging DynamoDB as global storage for multi-tenant config management, eliminating 30GB data across 20+ Kubernetes clusters and saving $2K/year.

◆ Increased reliability of long-running AI workflows by enhancing the core Java Spring Boot Temporal service, unifying error handling for 59 activities into 4 standardized types and reducing manual retries by 98%.

◆ Built non-blocking customer usage logging in a FastAPI-based AI service for LangChain and Amazon Bedrock, streaming to CloudWatch Logs for real-time billing and cutting latency by 120ms.

◆ Developed a Python AWS Lambda with Cognito post-signup triggers to sync 100% of SSO users (Okta, Microsoft, Google) in real time to on-prem DBs via a custom REST API.

◆ Owned integration testing with real mock events for Lambdas and Spring Boot services, boosting test speed by 40%.

Software Engineer Intern - AI

◆ Built 2 microservices using Java Spring Boot processing over 1M+ medical diagnosis records monthly. ◆ Streamed real-time data from a Spring service via RabbitMQ to the notification service and 2 Kafka topics for the ML team’s analysis and ETL processes, reducing failures and data loss by over 10GB+ during peak stages. ◆ Optimized data retrieval with Redis caching, reducing database calls by 65% and latency by 2s/API request, while aggregating 100K+ user data from PostgreSQL and reports data from MongoDB. ◆ Wrote unit tests using JUnit and Mockito, boosting coverage by 46% and reducing errors in microservices.

Backend Developer Intern

◆ Built Express.js APIs to transform data from 4 third-party sources, reducing processing time by 40% for DE team pipelines via async operations. ◆ Fixed 7+ critical Express.js API bugs using UUID tracing and structured logging, cutting production failures by 40% through close QA and DE team collaboration.

Software Engineer Intern

◆ Building a role-aware RAG powered Agentic AI cybersecurity chatbot using Python Lambdas, AWS Bedrock, and LangGraph for multi-step tool calling (32 tools).

◆ Reduced OpenSearch (VectorDB) indexing costs of 1M+ AWS S3 records of vulnerability scanners by $30K annually by building 7 serverless ingestion Lambda functions.

◆ Improving AI agent accuracy by LLMOps driven evals optimizing prompts and tools to reduce hallucinations by 95%.

◆ Automated 10K+ security records/day ingestion using EventBridge triggers and IaC with TypeScript via CDK.

AI Software Engineer

◆ Built 2 Java Spring Boot microservices to ingest, validate, and serve 2M+/yr medical-diagnosis records.

◆ Streamed real-time data from a Spring service via RabbitMQ to the notification service and 2 Kafka topics for the ML team’s analysis and ETL processes, reducing failures and data loss by over 10GB+ during peak stages.

◆ Optimized data retrieval with Redis caching, reducing database calls by 65% and latency by 2s/API request, while aggregating 100K+ user data from PostgreSQL and reports data from MongoDB.

◆ Developed integration tests using Mockito, increasing code coverage by 46% and reducing prod bugs significantly.

Backend Engineer Intern

Software Engineer with experience in AI, backend systems, and AWS cloud infrastructure.

Know about Chandrakant Koneti's work experience, education, bootcamps, certifications, and much more. Chandrakant Koneti