MA
Manav Amin Software Engineer
Software Engineer • Backends & Applied AI

Building resilient backends.
Shipping scalable intelligence.

I'm Manav Amin, a Computer Science graduate (WGU) based in Charlotte, NC. I engineer high-throughput microservices in Java / Spring Boot, deploy supervised ML prediction pipelines in Python, and automate cloud infrastructure with Docker & CI/CD.

View Case Studies Direct Inquiry Executive CV (PDF)
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% Data Integrity
0
% Cloud Provisioning Cut
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% Deadline-Miss Drop
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% Mission Completion Rate
engineer_terminal.sh ONLINE
Branch: main JUnit 5: 100% Pass Region: US-East
// 01. Case Studies

Selected Systems & Deployments

Explore verified enterprise microservices, applied machine learning architectures, and production automations.

CLOUD MICROSERVICES VERIFIED BUILD

Cloud-Native Enterprise Scheduling Platform

Re-architected a legacy hotel scheduling monolith into a containerized microservice platform with multithreaded i18n rendering, multi-currency formatting, and IANA timezone conversion.

~70% CUT
Cloud Provisioning Latency
ZERO DOWNTIME
GitLab CI/CD on AWS ECS & Azure
Java Spring Boot Docker Compose AWS ECS GitLab CI/CD
MACHINE LEARNING CS CAPSTONE

Predictive Intelligence Platform

Supervised regularized linear regression engine with custom 4-tier fallback inference for completion-time estimation under cold-start, sparse-data conditions.

Deadline-Miss Drop: ~30% in Simulation
Incremental online retraining with ACID MySQL writes.
Python scikit-learn Node.js MySQL
DOMAIN-DRIVEN DESIGN

Scalable Microservice Backend

Built from formal UML specs across controller, service, repository, and persistence layers. Verified across 50+ live transactions with JUnit 5/Mockito.

✓ 100% Data Integrity • Zero Post-Launch Defects
Spring Data JPA
NLP & AUTONOMOUS AGENTS

Autonomous Robotics & AI

Multi-domain conversational intent engine (200+ sessions) paired with a sensor-fused agent using Dijkstra pathfinding and Bayesian modeling.

✓ 92% Mission Completion in Disaster Simulation
CoppeliaSim
ETL & BUSINESS INTELLIGENCE

Automated Inventory BI Suite

Automated reconciliation pipelines with Pandas and Excel APIs; built SQL-backed real-time sales dashboard via Google Sheets API and JS triggers.

✓ Saved 3+ hrs/week • Cut shrinkage errors ~25%
Python Pandas
// 02. Architecture & Code Quality

Defensive Code in Production

Inspect snippets of actual patterns: Domain-Driven JPA, regularized fallback inference, and CI/CD pipelines. 


// Spring Boot Service with Domain-Driven Design & DTO Projection
@Service
@Transactional(readOnly = true)
public class SchedulingServiceImpl implements SchedulingService {

    private final ReservationRepository reservationRepository;
    private final TimezoneConverter timezoneConverter;

    @Autowired
    public SchedulingServiceImpl(ReservationRepository repo, TimezoneConverter tz) {
        this.reservationRepository = repo;
        this.timezoneConverter = tz;
    }

    @Override
    public List<ScheduleResponseDTO> getActiveSchedules(String clientTimezone) {
        return reservationRepository.findActiveByCurrentWindow()
            .stream()
            .map(entity -> ScheduleResponseDTO.builder()
                .id(entity.getId())
                .localizedStartTime(timezoneConverter.toUserZone(entity.getUtcStart(), clientTimezone))
                .formattedCurrency(entity.getLocalizedPrice())
                .status(entity.getStatus())
                .build())
            .collect(Collectors.toUnmodifiableList());
    }
}
// 03. Core Competencies

Technical Inventory

Technologies and tools verified by working builds, client deliverables, and academic capstones.

Backend & Cloud

Java / Spring Boot Advanced
Spring Data JPA / Hibernate Advanced
Docker / Docker Compose Production
AWS (ECS) / Azure Deployments
GitLab CI/CD / GitHub Automated

AI, ML & Data

Python / scikit-learn Supervised ML
Pandas ETL Pipelines Automated
NLP Intent Classification Production
Sensor Fusion & Robotics Simulation
Bayesian & Dijkstra Logic Applied

Languages & DBs

Java, Python, C++, SQL Core
JavaScript, Dart, PHP Proficient
MySQL / PostgreSQL ACID Writes
Cloudflare D1 / R2 / Workers Edge
Flutter / Mobile UI Cross-Platform
// 04. Trajectory

Experience History

DevOps Intern

Jun 2024 – Present
ADM Reliant
  • Delivered 4+ simultaneous client projects 100% on time in Java, C++, and HTML/CSS with zero critical post-launch defects.
  • Reduced revision cycles by ~40% by introducing peer review gates, regression test checklists, and sprint retrospectives within Agile/Scrum.
  • Translated requirements from 4+ concurrent stakeholders into scoped technical specifications using UML diagramming and iterative prototypes.

Store Manager & Automation Lead

Jan 2026 – Present
Savi Provisions LLC
  • Built a Python-automated inventory reconciliation system (Pandas pipelines, Excel API integration), cutting manual audit time by 3+ hrs/week and shrinkage errors by ~25%.
  • Engineered a SQL-backed real-time sales dashboard via Google Sheets API, JavaScript triggers, and REST data sync, boosting high-margin SKU sell-through by 20%.
  • Directed 5+ staff using Agile-modeled sprint cycles, structured KPI tracking, and iterative feedback loops.

B.S. in Computer Science

March 2026
Western Governors University

Relevant coursework: Artificial Intelligence, CS Capstone (ML), Advanced Java, Back-End Programming, Full-Stack Development.

// 05. Milestone Pricing

Project Estimator

Configure project scopes with transparent milestone quotes and real-time USD/INR conversions.

Milestone Configuration

Select required engineering modules

Fixed Milestone Scope
Estimated Milestone Investment
$240 (approx. ₹21,120 INR)
Estimated effort: ~15 engineering hours
Lock in this Milestone
// 06. Direct Connect

Initiate a Project Conversation

I am currently accepting select freelance contracts, backend architectural engagements, and machine learning prototypes.

Email Address
manavamin7@gmail.com
Direct Phone
(276) 469-5980
Location
Charlotte, NC (Available Globally for Remote Delivery)

Send Project Brief

Share your target requirements and milestones. Typical response time is under 24 hours.

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