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 2025 – Present · 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 2021 – Jan 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.