R

Rakshith Rapaka

Software Engineer

United States4 yrs 1 mo experience

Key Highlights

  • Expert in modernizing backend systems for high-volume processing.
  • Proficient in developing scalable applications using Java and Python.
  • Strong experience in implementing CI/CD and cloud infrastructure.
Stackforce AI infers this person is a Fintech Backend Engineer with expertise in scalable systems and cloud infrastructure.

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Skills

Core Skills

Java Spring BootPythonAngularAwsPytestHugging FaceJavaReactC++PostgresqlDocker

Other Skills

API DevelopmentAlgorithmsApache AirflowC (Programming Language)C#Cascading Style Sheets (CSS)CommunicationData AnalysisData StructuresGitLabHTMLJavaScriptJenkinsKafkaKubernetes

About

I work with technology, and I love what I do.

Experience

Freddie mac

Software Engineer II

Jan 2025Present · 1 yr 2 mos · United States

  • Modernized a core loan-eligibility service by rebuilding backend components in Java Spring Boot and restructuring API contracts, allowing underwriting workflows to handle heavier request volumes without queue backlogs during peak filing windows.
  • Developed new underwriting review screens using Angular and TypeScript for internal risk officers, integrating advanced filtering and real-time event updates from Kafka so analysts could complete validation steps in fewer handoffs.
  • Designed asynchronous data pipelines using Python, Apache Airflow, and Snowflake to streamline ingestion of appraisal, borrower, and credit-risk files, reducing the nightly processing duration from several hours to a fraction of that time.
  • Led the migration of a legacy mortgage-rules engine from on-prem WebLogic to AWS ECS, refactoring configuration layers, secrets handling, and build workflows so the platform could run consistently across cloud environments.
  • Created an internal testing framework using PyTest and Playwright to cover service-level checks, UI flows, and cross-service contract validation, helping teams catch integration issues earlier and stabilize pre-release cycles.
  • Implemented a document-embedding pipeline using Hugging Face models to classify appraisal narratives and flag anomalies, giving the risk team faster access to structured signals during high-volume review batches.
  • Built reusable Python scripts for S3 lifecycle checks, feature-flag rollouts, and build-artifact verification, reducing routine operational work for the team and improving the reliability of weekly releases.
  • Integrated a retrieval-augmented generation workflow using Hugging Face embeddings and internal mortgage-policy documents, enabling risk specialists to surface contextual explanations instantly for complex underwriting scenarios.
Java Spring BootAPI DevelopmentAngularTypeScriptPythonApache Airflow+5

Goldman sachs

Software Engineer I

Feb 2021Jan 2024 · 2 yrs 11 mos · India

  • Managed core backend services in Java for trading and risk systems, scaling the order processing pipeline to handle 18,000 requests per minute and keeping end-to-end trade-acknowledgement latency below 120 ms during market hours.
  • Programmed React micro-frontends for portfolio and execution tools, reducing analyst workflow steps from 9 to 4 and cutting average time to generate client reports by 2 minutes.
  • Engineered resilient APIs with Java Spring Boot and Node.js, adding regional routing and circuit breakers to preserve data integrity across 3 continents and ensure uninterrupted data delivery during peak loads.
  • Reworked legacy valuation modules in C++ and C#, refactoring compute-heavy routines to reduce memory footprint by 700 MB and bringing nightly valuation failures down to single-digit occurrences per month.
  • Automated operational runbooks with Python for log analysis, environment validation, and anomaly detection, reducing mean-time-to-detect incidents by 45 minutes and improving on-call transitions.
  • Optimized data storage in PostgreSQL and MongoDB through partitioning and read replicas, increasing sustained read/write throughput by 10× during settlement batches and stabilizing processing windows.
  • Containerized services with Docker and orchestrated deployments on Kubernetes, implementing health checks and automated failover that achieved <1-minute recovery targets and supported multiple daily releases.
  • Strengthened CI/CD and cloud infrastructure using GitLab, Jenkins, Terraform, and AWS by adding integration-test gates and artifact signing so deployments across development, staging, and production completed reliably within single-digit minutes.
  • Constructed internal generative-AI utilities using large language models for code summarization, log triage, and test-case generation, applying prompt-engineering techniques to improve accuracy and reduce manual review cycles.
JavaReactNode.jsC++C#Python+8

Education

University at Buffalo

Master's degree — Artificial Intelligence

Jan 2024May 2025

Ramaiah Institute Of Technology

Bachelor of Engineering - BE — Electronics and communications

Jan 2017Jan 2021

Sainik School Korukonda - SSK

12th — PCMC

Jun 2015Mar 2017

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