Aarush Thapa Software Engineer
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Engineering

How I Build Production-Grade Software

I care about software that keeps working after launch. My focus is clear architecture, reliable delivery, and clean handover so teams can trust the system and keep moving.

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Professional Workflow
My Development Workflow
  1. 01

    Requirements

    Intent, constraints, and success criteria.

    Scoped brief
  2. 02

    Domain Research

    Workflow reality, data risk, failure cost.

    Domain baseline
  3. 03

    Prototype + Validate

    Spreadsheet flow first, POC second.

    Validated model
  4. 04

    Specification

    APIs, data rules, architecture notes.

    Technical spec
  5. 05

    Development

    Iterative build, review discipline, safe migrations.

    Release-ready increment
  6. 06

    Testing

    Logic, integration, and user-flow coverage.

    Risk-tested build
  7. 07

    Delivery + Handover

    Rollout plan, rollback path, usable docs.

    Production handover
Engineering Commitments
What You Can Expect When I Own Delivery

Reliability built in from day one

I design retries, fallback behavior, and idempotency early so failures are controlled, visible, and recoverable.

Single-threaded ownership

I own the full path from requirements to release and handover, so execution does not fragment across teams.

Maintainable after launch

I ship code and docs another engineer can understand quickly, extend safely, and operate without hidden context.

AI used with engineering control

I use AI to accelerate validation and implementation, but architecture and correctness decisions stay under explicit human review.

Toolchain
From Discovery to Deployment and Monitoring
01

Discovery

Requirements, scope boundaries, risk framing, and decision records before design lock.

  • Requirement briefs
  • Scope maps
  • Decision logs
02

Validation

Domain workflow analysis, spreadsheet modeling, and proof-of-concept loops.

  • Domain process maps
  • Excel prototyping
  • POC feedback cycles
03

Architecture + Build

Service boundaries, data modeling, API contracts, and implementation through terminal-first dev flow.

  • Python / Django
  • PostgreSQL / Redis / Celery
  • Vim / VS Code / terminal automation
04

Quality

Layered tests and release-readiness checks from core logic to integration behavior.

  • Unit coverage
  • Integration checks
  • Regression gates
05

Deployment

Controlled rollout, migration safety, rollback planning, and release communication.

  • GitHub CI/CD
  • Staged rollout
  • AWS, VPS
06

Monitoring + Ops

Operational visibility after launch through health checks, logs, alerts, and incident handling.

  • Health dashboards, Grafana
  • Error tracking, Sentry
  • Incident response notes
Industry Domains
Domain Playbooks
Cross-Industry Method

Same engineering standards, adapted to domain constraints

I do not copy one solution across industries. I keep the same engineering discipline and change the operating model based on failure cost, data sensitivity, and workflow complexity.

  • Map where failure hurts most
  • Design controls for that failure mode
  • Ship with operational visibility

Ecommerce

Checkout and order resilience

Remove payment and order-state fragility under real transaction load.

  • Idempotent payment handling
  • Recovery-safe order transitions

POS and Retail

Sync integrity across channels

Keep transactions and inventory consistent between store and central systems.

  • Conflict-aware sync rules
  • Reconciliation loops

Restaurant and Hospitality

Order flow and service operations

Stabilize dine-in, takeaway, and kitchen workflows with clear status and handoff points.

  • Order lifecycle tracking
  • Queue and dispatch visibility

HR and People Operations

Employee lifecycle and workflow integrity

Improve hiring, onboarding, and internal request workflows with clear ownership and auditability.

  • Role-based process controls
  • Approvals and traceable actions

Infrastructure

Service reliability operations

Balance scale and stability with measurable health signals and controlled release paths.

  • Monitoring and alerting baselines
  • Rollback-aware deployments

Portfolio and Service Ops

Execution and handoff structure

Reduce execution drag with consistent scoping, milestoneing, and transfer rules.

  • Milestone-based delivery
  • Repeatable handoff protocols
Also worked in
Real Estate Lead Management Logistics
Proof
Case Studies
Case Study

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Personal projects remain available. This page shows how I think, build, and deliver as an engineer.

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