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Himanshu Kiran Garud

Mid-Level Software Engineer specializing in full-stack web and data engineering

EPRIUniversity of North Carolina at CharlotteUnited States4 Years ExperienceMid LevelWorks On-Site

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About

Backend/ML engineer who has built both enterprise data pipelines and real-time AI products: modular Python (Flask/FastAPI) services integrating automation scripts and low-latency ML inference (MediaPipe, PyTorch) plus OpenAI-powered feedback. Demonstrated measurable performance wins (~30% faster HR workflows; ~40% faster AWS pipelines across 100+ Oscar Health feeds) and strong multi-tenant/data-isolation patterns (schema-based isolation, RBAC, microservices).

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Key Strengths

  • Designed modular Python/Flask backend with stable endpoints and fallback/cached responses for unreliable automation scripts
  • Improved internal HR workflow backend performance by ~30% via WAL mode, batched writes, and retry logic
  • SQLAlchemy/PostgreSQL performance tuning using access-pattern-driven schema design, composite indexes, and materialized views
  • Built real-time ML backends (FastAPI) with low-latency inference using multithreading, batching, and model warmup
  • Integrated GPT/OpenAI API layer to generate personalized feedback from ML outputs
  • Implemented schema-based data isolation and RBAC/scoped queries for multi-tenant-style systems
  • Optimized high-throughput background pipelines on AWS (100+ production feeds) with parallel modular tasks, caching, and retries; ~40% faster processing
  • Designed and evolved backend data processing system supporting 100+ production data feeds for US healthcare clients
  • Restructured monolithic workflows into modular microservices (Spring Boot/Python) on AWS and GCP
  • Led production migration from GraphQL services to optimized SQL-driven microservices with zero downtime via parallel run and feature-flag rollout
  • Improved API response time by ~30% through query and architecture changes
  • Increased distributed pipeline throughput by ~40% via better orchestration and monitoring
  • Strong data integrity and risk controls (checksum validation, automated reconciliation, schema validation)
  • Built reliable low-latency FastAPI services using Pydantic schemas, async endpoints, and predictable error handling
  • Implemented layered, evolvable security (JWT auth, RBAC, row-level scoping, centralized middleware, rate limiting, logging)
  • Identified subtle ETL failure mode (partial datasets passing schema checks) and added completeness/time-window validations to prevent downstream corruption

Reference Highlights

Strongly Recommended
  • Strong backend technical depth
  • Designs clean, modular backend components
  • Writes reliable production-grade Python code
  • Clear reasoning about API structure and data flow
  • Good Flask practices beyond basics
  • Strong SQLAlchemy/ORM skills
  • Optimizes database queries effectively
  • Balances speed and maintainability without overengineering
  • Designs scalable, maintainable architectures under startup pressure
  • Breaks complex workflows into clean, testable services
  • Comfortable making decisions and iterating quickly
  • Adapts designs as requirements evolve
  • Very effective cross-functional collaborator
  • Clear communicator who keeps teams aligned
  • Proactive about sharing progress and clarifying requirements
  • Approachable and solution-oriented
  • Calm and methodical under tight deadlines
  • Finds root causes (not just symptoms)
  • Delivers clean fixes without introducing new issues
  • Verifies fixes end-to-end
  • Reliable teammate
  • Strong technical depth in Python
  • Writes clean, well-structured, maintainable code
  • Thoughtful software development with strong fundamentals
  • Designs modular, scalable, easy-to-extend components
  • Balances fast iteration with clean engineering practices
  • Breaks down complex workflows into manageable, reusable pieces
  • Collaborates extremely well in fast-moving AI environments
  • Clear communicator across faculty, peers, and students
  • Adapts quickly as project directions shift
  • Calm and focused under pressure
  • Efficient, thorough debugging and problem-solving
  • Steps up to coordinate and deliver working end-to-end systems for deadlines

Himanshu's references speak for themselves. Let us connect you.

Experience

Software Engineer Co-opEPRI · Jun 2024 – May 2025contract
Research AssistantUniversity of North Carolina at Charlotte · Jan 2024 – May 2024part-time
Software EngineerPersistent Systems · May 2021 – Jul 2023
Software Engineer InternEastro Control Systems · Dec 2019 – Feb 2020internship
Software EngineerRebecca Everlene Trust Company · Jul 2025 – Present

Education

University of North Carolina at Charlottemaster, Computer Science (2025)

Awards

  • 1st Place, Hack with CAIR Hackathon - UNC Charlotte
  • Top 20 Finalist, HooHacks Hackathon 2025
  • High Five Award - Persistent Systems

Languages

English

Certifications

Microsoft Certified: Azure AI Engineer Associate (AI-102)AI-102 Azure AI Engineer Associate

Publications

2 publications

Depression classification using machine learningFake news detection using machine learning

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Himanshu Kiran GarudMid-Level Software Engineer specializing in full-stack web and data engineering