Pradeep Rathore
Frontend Developer
Node JS
SQL
TypeScript
JavaScript
API Design: 4/10
System Architecture: 4/10
Active 9 days ago
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Overview
Technical skills
Timeline
Roles
Overview
A pragmatic Middle-level SDET focusing on building validation, simulation, and evaluation pipelines for generated application schemas. The strongest proven skill is schema-level validation and execution simulation as implemented in ai-app-generator/backend/pipeline/schemaGenerator.js and ai-app-generator/backend/utils/executionSimulator.js. The public code lacks CI-integrated test automation, formal contract-testing tools, and dedicated performance/load testing harnesses.
Technical skills
Node JS• Middle
SQL
TypeScript• Middle
JavaScript• Middle
Node JS
Express
Mongoose
Axios
Databases
MySQL
MongoDB
PostgreSQL
Frontend
Redux
Redux Toolkit
PostCSS
Vite
Chart.js
Next.js
React.js
Tailwind CSS
DevOps
Vercel
Git
Rest API
QA
Postman
QA
Timeline
Frontend Developer Intern
•
Junior
Rent-Par
•
Internship
Developed and shipped responsive web interfaces using React and Next.js, translating UI requirements into reusable component structures. Integrated REST APIs end-to-end and improved rendering and page responsiveness across devices. Built a reusable Tailwind CSS-based component system to reduce duplicate UI work. Collaborated in an Agile workflow using Git/GitHub with sprint planning and code reviews.
React.js
Next.js
Tailwind CSS
Rest API
Git
AI App Generator
Website
Built an AI-powered application generator that converts natural-language prompts into functional UI scaffolding. Integrated external AI APIs into a Next.js/React pipeline and designed a fully responsive interface with reusable, prompt-driven components to accelerate app prototyping.
Rajiv Gandhi Proudyogiki Vishwavidyalaya (Rajiv Gandhi Technical University)
Bachelor's Degree •
Computer Science
Middle Backend Developer
Confidence: Medium API Engineer
A Middle-level backend engineer focused on API/schema-driven toolchains and Node.js-based pipelines with a strength in designing end-to-end generation and validation stages. The strongest proven skill is pipeline design and offline evaluation as implemented in ai-app-generator/backend/pipeline/schemaGenerator.js and ai-app-generator/backend/utils/executionSimulator.js which show modular schema creation, validation and execution simulation. The codebase does not evidence production-grade runtime resilience, database migrations, or secure token lifecycle management and contains some simple Express endpoint issues and missing input sanitization at HTTP boundaries.
API Design
4/10
How well APIs are designed
API design shows deliberate schema generation and a consistent error contract plus a basic rate-limit hint, but lacks idempotency, pagination design, and runtime enforcement.
Evidence
ai-app-generator/backend/pipeline/schemaGenerator.js: generateAPISchema (baseUrl, endpoints, errorHandling, rateLimit)
ai-app-generator/backend/pipeline/validator.js: validateAPISchema (endpoint method/path checks, duplicate detection)
Data Layer & Database
3/10
Working with databases
Data layer is specified at the schema level with PKs, FKs and indexes and cross-checks, but there is no migration history, raw SQL tuning, transaction handling or DB connection code.
Evidence
ai-app-generator/backend/pipeline/schemaGenerator.js: generateDBSchema (tables, primaryKey, foreignKey, indexes, driver: 'postgresql')
ai-app-generator/backend/pipeline/validator.js: validateDBSchema (primary key checks, duplicate table/field checks)
ai-app-generator/backend/utils/executionSimulator.js: simulateDatabase (validates PKs, FK references)
Scalability & Performance
2/10
Handling load and speed
Some performance awareness appears in schema metadata and evaluator metrics, but there is no evidence of caching, connection pooling, queueing, or measured load optimizations.
Evidence
ai-app-generator/backend/pipeline/schemaGenerator.js: generateAPISchema (rateLimit field included in generated API schema)
ai-app-generator/backend/utils/evaluator.js: metrics and averageLatency calculations
ai-app-generator/backend/utils/executionSimulator.js: generateSuggestions (recommends adding rate limiting)
System Architecture
4/10
Overall system structure
A clear multi-stage pipeline (intent extraction, system design, schema generation, validation, repair, simulation, evaluation) shows modular boundaries and design thinking, but it is an in-repo pipeline rather than a distributed service architecture with contracts and operational controls.
Evidence
ai-app-generator/backend/pipeline/schemaGenerator.js: Schema generation stage implementation
ai-app-generator/backend/pipeline/validator.js: Validation stage implementation
ai-app-generator/backend/utils/executionSimulator.js: Execution simulation stage implementation
Security & Auth
3/10
Protecting data and access
Auth is modeled in generated schemas (roles, session config, HS256 algorithm) and checked for consistency, but there is no evidence of token lifecycle handling, refresh/revocation, secrets management, or secure runtime enforcement.
Evidence
ai-app-generator/backend/pipeline/schemaGenerator.js: generateAuthSchema (roles, sessionConfig.algorithm = 'HS256', expiresIn)
ai-app-generator/backend/pipeline/validator.js: validateConsistency (checks for users table when auth enabled)
ai-app-generator/backend/utils/executionSimulator.js: simulateAuthFlows (verifies login endpoint and users table)
Reliability & Observability
3/10
Stability and monitoring
The codebase includes evaluation, logging and reporting utilities that aid observability and offline reliability assessment, but lacks runtime resilience primitives like retries with backoff, circuit breakers, or structured distributed tracing.
Evidence
ai-app-generator/backend/utils/evaluator.js: metrics collection, generateReport and printReport
ai-app-generator/backend/utils/executionSimulator.js: executionLog, errors, and generateReport
ai-app-generator/backend/pipeline/validator.js: returns structured errors and warnings for validation
Expertise
Backend AI & LLM• Middle
Node.js• Middle
Microservices & API Architecture• Middle
Industries
Commerce• Middle
Education• Middle
Health Care• Middle
Software• Middle
Technologies
SQL
Node JS• Middle
MySQL
MongoDB
PostgreSQL
Rest API
Express
Axios
Recommendations
- Build prototype tooling that generates API and DB schemas from high-level specs and ships test harnesses for them (use the existing schemaGenerator, validator and evaluator as a foundation).
- Develop API-first backend services in Node.js that need clear schema contracts and offline simulation, such as internal dev tools or low-risk internal platforms.
- Harden generated backends by adding runtime components: input validation, structured logging/correlation ids, timeouts and retries with backoff, and secrets handling for auth tokens.
- Work on authoring migration chains and simple DB adapter layers to convert schema objects into safe, idempotent migrations for PostgreSQL.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Middle Frontend Developer
Confidence: Medium UI Engineer
Frontend UI-focused developer specializing in building responsive React and Tailwind-based interfaces. The strongest proven skill is handcrafted UI and visual polish driven by CSS and layout work, as seen in the dashboard and sidebar styles (responsive rules, focus-visible states and placeholder/error styles). There is limited evidence of componentized React architecture, advanced state management, automated tests, or performance profiling in public code.
UI Component Architecture
2/10
How interface parts are built
Minimal component architecture; most work is handcrafted CSS and static HTML with little evidence of modular React components, composition patterns, or a custom design-system package.
Evidence
portfolio/src/App.css
healthcare-dashboard/src/css/sidebar.css
Responsive & Cross-browser
4/10
Works on all screens and browsers
Reasonable responsive layout and dark-mode support using media queries and prefers-color-scheme; sidebar and grid layouts include mobile breakpoints and adaptive rules.
Evidence
healthcare-dashboard/src/css/sidebar.css
healthcare-dashboard/src/css/dashboard.css
portfolio/src/index.css
Performance Optimization
1/10
Speed of the interface
No measured performance work or explicit optimizations; uses Vite as the build tool but there is no evidence of profiling, bundle analysis, code-splitting strategy, or virtualization.
Evidence
portfolio/vite.config.js
healthcare-dashboard/package.json
Accessibility & Semantics
3/10
Usable for everyone
Some accessibility-conscious CSS (focus-visible outlines, focus styles, prefers-color-scheme) and visible loading/error classes, but no ARIA on custom widgets or keyboard/focus management logic in JS.
Evidence
portfolio/src/App.css
healthcare-dashboard/src/css/sidebar.css
healthcare-dashboard/src/index.css
State Management & Data Flow
1/10
Managing data in the app
Very light state/data-flow work: a fetch helper signature exists and there are simple form handlers, but no evidence of request cancellation, optimistic updates, cache invalidation, or state machines.
Evidence
healthcare-dashboard/src/services/api.js
Rathore_Construction/index.html
UX & Visual Polish
4/10
Look and feel quality
Good visual polish in CSS with consistent card patterns, hover/transition effects, placeholder/loading and error styles that improve perceived quality, but largely static without advanced UX patterns in JS.
Evidence
healthcare-dashboard/src/css/main-content.css
healthcare-dashboard/src/css/right-sidebar.css
healthcare-dashboard/src/css/sidebar.css
Expertise
React• Junior
HTML & CSS• Middle
Industries
Health Care• Middle
Technologies
Redux
Tailwind CSS
Next.js
Chart.js
Vercel
Git
React.js
Vite
Mongoose
PostCSS
Redux Toolkit
Recommendations
- Develop medium-complexity React dashboards and UI components that reuse the existing CSS patterns and Chart.js integrations.
- Convert static HTML/CSS sections into modular React components and establish a small design-system (tokens, shared components) for reuse.
- Implement robust data-fetch helpers with request cancellation, error and loading state handling, and add optimistic update patterns where appropriate.
- Add accessibility audits (axe or eslint-plugin-jsx-a11y) and automated tests for interactive components.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Middle QA Engineer
Confidence: Medium SDET
A pragmatic Middle-level SDET focusing on building validation, simulation, and evaluation pipelines for generated application schemas. The strongest proven skill is schema-level validation and execution simulation as implemented in ai-app-generator/backend/pipeline/schemaGenerator.js and ai-app-generator/backend/utils/executionSimulator.js. The public code lacks CI-integrated test automation, formal contract-testing tools, and dedicated performance/load testing harnesses.
Test Automation Frameworks
5/10
Building automated tests
Custom test infrastructure and execution simulation exist (test runner, dataset, simulator) showing deliberate test harness design, but lacks full fixture/data isolation, mock servers with logic, and parallelization/sharding configuration.
Evidence
ai-app-generator/backend/tests/testRunner.js
ai-app-generator/backend/tests/testDataset.js
ai-app-generator/backend/utils/executionSimulator.js
Test Coverage & Strategy
5/10
What and how to test
Evidence of risk- and edge-case oriented test datasets and an evaluation framework that records failures and readiness metrics; good negative-case design exists but no property-based tests, mutation testing, or quantified coverage indicators.
Evidence
ai-app-generator/backend/tests/testDataset.js
ai-app-generator/backend/utils/evaluator.js
ai-app-generator/backend/pipeline/validator.js
API & Integration Testing
4/10
Testing how parts work together
API schema generation, validation, and basic endpoint simulation are implemented, demonstrating API-focused QA thinking, but there is no formal contract-testing integration (Pact/schemathesis) or automated schema-driven API test harness.
Evidence
ai-app-generator/backend/pipeline/schemaGenerator.js
ai-app-generator/backend/utils/executionSimulator.js
ai-app-generator/backend/pipeline/validator.js
Performance & Load Testing
2/10
Testing speed under load
Latency and basic performance metrics are tracked in the evaluator, but there are no load/soak/spike scenarios, ramping profiles, SLO assertions, or dedicated performance tooling evidence.
Evidence
ai-app-generator/backend/utils/evaluator.js
ai-app-generator/backend/tests/testDataset.js
Bug Reporting & Analysis
2/10
Finding and describing bugs
There is structured reporting and logging for failures and readiness which aids analysis, but no evidence of formal bug reports, reproducible minimal repros, linked fixes, or documented flaky-test investigations.
Evidence
ai-app-generator/backend/utils/evaluator.js
ai-app-generator/backend/utils/executionSimulator.js
CI Test Integration
1/10
Running tests automatically
Local test scripts and npm test entries exist, but there is no CI workflow, test artifact collection, quarantine mechanics, selective test runs, or matrix configuration in the provided files.
Evidence
ai-app-generator/backend/package.json
ai-app-generator/frontend/package.json
Expertise
API Testing & Contract Validation• Middle
SDET & Test Engineering• Middle
Technologies
QA
JavaScript• Middle
TypeScript• Middle
Postman
Recommendations
- Add formal contract testing (schemathesis or Pact) and automated schema-driven API tests derived from schemaGenerator outputs to validate contracts end-to-end.
- Integrate the test runner and evaluator into CI with artifact capture (logs, JSON reports) and per-test retry/quarantine policies to manage flakiness.
- Introduce property-based or fuzz tests and consider mutation testing to raise test-honesty and find subtle validation gaps.
- Add a performance testing stage (k6 or Locust) with ramp profiles, SLO assertions, and percentile reporting to complement the existing latency metrics.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories: