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santhosh ravula

Mid-level Full-Stack Software Engineer specializing in cloud-deployed web apps and APIs

Dayton, OHSoftware Engineer3 years experienceMid-LevelFinancial ServicesFinTechWeb Development
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About

Software engineer who has shipped both core web platform features (secure user authentication/profile management) and production LLM systems. Built an internal documentation knowledge assistant using a full RAG pipeline (OpenAI embeddings, vector DB, semantic search, reranking) with evaluation loops and a scalable document-ingestion pipeline for PDFs/FAQs, iterating based on metrics and user feedback.

Experience

Software EngineerWells Fargo
Software DeveloperNexova
Software Engineer (AI-Integrated Backend)Wells Fargo

Education

Wright State Universitymaster, Computer Science (2025)
TKR College of Engineeringbachelor, Computer Science
TKR College of Engineering and Technologybachelor, Computer Science & Engineering

Key Strengths

  • Owned end-to-end delivery of authentication and profile management (API/db/frontend/deploy)
  • Balanced security vs delivery speed; shipped JWT-based auth with secure practices (hashing, token expiry, protected routes)
  • Improved signup conversion by simplifying registration flow and clarifying validation/error messaging; reduced support requests
  • Built production RAG knowledge assistant using embeddings + vector DB + semantic retrieval with guardrails (fallbacks, rate limiting, auth)
  • Designed LLM evaluation loop (100-query test set, relevance/correctness/hallucination metrics, human review) and iterated based on results
  • Improved retrieval quality by adjusting chunking strategy and adding reranking to reduce irrelevant context
  • Built scalable ingestion pipeline for messy docs (PDF/FAQs/guidance) with queue-based reliability, retries, and observability
  • Designed and implemented end-to-end inference pipeline (ingestion to model execution to API exposure)
  • Optimized inference speed/accuracy tradeoffs with scalable architecture
  • Reduced/controlled latency via timestamped logging, bottleneck identification, and replaying recorded data
  • Implemented asynchronous request handling for more stable real-time response times
  • Containerized services with Docker for scalable deployment on AWS
  • Built ROS Python perception nodes (subscribe to image topics, run OpenCV/ML inference, publish results)

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Languages

English

Skills

PythonJavaScriptTypeScriptSQLReactAngularHTML5CSS3FlaskDjangoREST APIsPostgreSQLMySQLMongoDBAWS