Pratanu Dolui
Software Developer
Management: Under a year (1-5 people)
Java
JavaScript
Node JS
Python
SQL
API Design: 4/10
Data Layer & Database: 4/10
Active 3 days ago
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Overview
Technical skills
Projects
Timeline
Roles
Overview
A Middle-level backend/API engineer with strong practical experience building small REST services and integrations and solid algorithmic programming skills. The strongest proven skill is implementing pragmatic API endpoints and DB access, evidenced by restaurant-incident-tool/app/api/incidents/route.js and the associated lib/db.js prepared-statement usage. The public code shows limited evidence of production-grade concerns like service-level architecture, migrations, distributed systems, comprehensive observability, or robust auth for public endpoints.
Phone
Technical skills
Languages
5
Java
JavaScript
Node JS
Python
SQL
Frontend
3
React.js
Next.js
Tailwind CSS
AI/ML
4
Machine Learning
Claude
NumPy
Datasets
Other
24
Django
MySQL
Axios
Express
AWS
Hibernate
Spring Boot
Oracle
Gemini
Kubernetes
Spring MVC
GitHub
JUnit
SQLite
Dotenv
ESLint
Docker
Git
Rest API
Postman
Maven
Copilot
Cursor
Jupyter Notebook
Projects
– Built a full-stack clinic management SaaS application using Spring Boot and MySQL.
– Designed REST APIs for appointments, billing, authentication, and medical records.
– Implemented backend business logic using Hibernate and optimized SQL queries.
– Technologies: Java, Spring Boot, Hibernate, REST APIs, MySQL, Git
Java
Spring Boot
Hibernate
Rest API
MySQL
Git
– Developed an e-commerce application with authentication and order workflows.
– Built REST-based backend services using Spring Boot and Hibernate.
– Technologies: Java, Spring Boot, Hibernate, REST APIs, MySQL
Java
Spring Boot
Hibernate
Rest API
MySQL
– Developed a Quantum Machine Learning model for insurance risk analysis.
– Performed predictive analysis and data-driven optimization workflows.
– Technologies: Django, QML, Machine Learning, Data Analysis
Django
Qiskit Machine Learning
Q# Machine Learning Library
Timeline
Visvesvaraya Technological University (VTU)
Bachelor's Degree •
Computer Science and Engineering(CSE)
Backend Development Intern
•
Junior
Dhee Coding Lab
•
Internship
Built Java backend APIs for SaaS-style web applications using Spring Boot, Hibernate, and MySQL. Implemented REST endpoints with CRUD operations, authentication modules, and optimized SQL queries. Collaborated using Git in an Agile workflow. Performed API testing and debugging with Postman and JUnit.
Java
Spring Boot
Hibernate
MySQL
Rest API
Git
GitHub
Postman
JUnit
SQL
Virtual Internship
•
Junior
CODESOFT
•
Internship
Developed responsive web interfaces using HTML, CSS, and JavaScript with MySQL integration. Worked on frontend components and form handling while improving UI responsiveness. Supported end-to-end web development tasks as part of a short virtual internship.
JavaScript
MySQL
Middle Backend Developer
Confidence: Medium API Engineer
A Middle-level backend/API engineer with strong practical experience building small REST services and integrations and solid algorithmic programming skills. The strongest proven skill is implementing pragmatic API endpoints and DB access, evidenced by restaurant-incident-tool/app/api/incidents/route.js and the associated lib/db.js prepared-statement usage. The public code shows limited evidence of production-grade concerns like service-level architecture, migrations, distributed systems, comprehensive observability, or robust auth for public endpoints.
API Design
4/10
How well APIs are designed
Solid REST API endpoints with query-based filtering and input validation, but no versioning, idempotency keys, or documented error contract; APIs are pragmatic but lack production-grade API governance.
Evidence
restaurant-incident-tool/app/api/incidents/route.js: GET uses query params for filtering and POST validates required fields
restaurant-incident-tool/app/api/incidents/[id]/route.js: PATCH validates status and DELETE checks existence before delete
salesforce-validation-app/server/index.js: /api/token and /api/toggle implement Salesforce OAuth token exchange and remote API calls
Data Layer & Database
4/10
Working with databases
Uses better-sqlite3 with prepared statements and pragmatic queries; shows correct use of parameter binding and basic data integrity checks but no migration history, transactions, or explicit schema evolution artifacts.
Evidence
restaurant-incident-tool/app/api/incidents/route.js: uses db.prepare(...).run and .all with parameterized queries
restaurant-incident-tool/app/api/incidents/[id]/route.js: checks existence via SELECT before DELETE
restaurant-incident-tool/lib/db.js: getDb abstraction (imported and used by API routes)
Scalability & Performance
2/10
Handling load and speed
Limited scalability patterns; single-process SQLite backend and sequential client-side deploy calls indicate no queueing, caching or rate-limiting strategy for higher loads.
Evidence
restaurant-incident-tool/app/api/incidents/route.js: direct DB calls to SQLite (better-sqlite3) without pagination or caching
salesforce-validation-app/src/App.js: deployChanges issues sequential fetch calls per rule (no batching/queueing)
System Architecture
3/10
Overall system structure
Reasonable separation of frontend and backend, a small modular API layer and a DB helper, but architecture is single-service and lacks service contracts, config layering, or multi-service decomposition rationale.
Evidence
restaurant-incident-tool/app/dashboard/page.js and app/report/page.js: clear frontend pages consuming API
restaurant-incident-tool/app/api/incidents/route.js and lib/db.js: separate API and DB abstraction
Security & Auth
3/10
Protecting data and access
Shows security awareness in places (PKCE on the client, server-side token exchange, prepared statements) but also has gaps like absent authentication on incident APIs, absolute dotenv path leaking local path, and missing robust secret handling and request timeouts.
Evidence
salesforce-validation-app/src/App.js: generates code_verifier and code_challenge for PKCE
salesforce-validation-app/server/index.js: server-side token exchange with client_secret and use of axios for Salesforce calls
restaurant-incident-tool/app/api/incidents/route.js: input validation and use of parameterized SQL queries
Reliability & Observability
2/10
Stability and monitoring
Basic error handling and console logging exist and client integrates reportWebVitals for perf metrics, but there is no evidence of retries/backoff, timeouts, structured tracing, correlation ids, or graceful shutdown logic.
Evidence
salesforce-validation-app/server/index.js: try/catch with console.error for axios failures but no retry/timeouts configuration
salesforce-validation-app/src/reportWebVitals.js: integrates web-vitals for performance measurement
restaurant-incident-tool/app/api/incidents/route.js: catches AI call failures and falls back to null summary
Expertise
Backend AI & LLM• Middle
Java• Middle
Node.js• Middle
Industries
Food & Beverages• Middle
Technologies
Java• since 2025 • Middle
Rest API• since 2025
Maven
Spring Boot• since 2025
Hibernate• since 2025
Django• since 2024
Spring MVC
Dotenv
Recommendations
- Build and maintain small-to-medium REST services and internal admin tooling that integrate third-party APIs (OAuth flows, CRUD with SQLite/Postgres).
- Implement LLM-assisted backend features and safe prompt handling, expanding the Anthropic integration into robust request/response validation and error handling.
- Develop Node.js APIs with improved operational maturity: add request timeouts, retries/backoff, structured logging, and authentication/authorization.
- Work on Java-based algorithmic components or performance-sensitive services where strong data-structure and algorithm skills are required.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Middle Frontend Developer
Confidence: High Fullstack
Fullstack frontend engineer (mid-level) specializing in building React and Next.js applications with backend API integrations. The strongest proven skill is integrating frontend interfaces with backend services and third-party APIs, demonstrated by the Salesforce OAuth PKCE flow and token exchange (salesforce-validation-app/src/App.js and server/index.js) and the Next.js API routes with DB integration (restaurant-incident-tool/app/api/incidents/route.js). There is limited public evidence of automated tests, CI pipelines, advanced security hardening, or measured performance benchmarking.
UI Component Architecture
4/10
How interface parts are built
Component structure is page-focused with sensible composition and local hooks; there is not a dedicated design-system or many isolated atomic components, but components are organized and reused across views.
Evidence
restaurant-incident-tool/app/dashboard/page.js: dashboard UI broken into logical sections with small render functions and mapped cards
salesforce-validation-app/src/App.js: centralized App component managing login, rule list, toggles and pendingChanges
Responsive & Cross-browser
4/10
Works on all screens and browsers
Responsive layouts use Tailwind responsive utilities and there is some attention to reduced-motion; no advanced feature-detection or RTL/i18n scaffolding was found.
Evidence
restaurant-incident-tool/app/dashboard/page.js: grid/grid-cols responsive layout and utility classes
salesforce-validation-app/src/App.css: prefers-reduced-motion media query
Performance Optimization
3/10
Speed of the interface
Shows performance awareness in places (deferred web-vitals import, useCallback to stabilize fetch), but lacks measured optimization artifacts, bundle analysis, or list virtualization.
Evidence
salesforce-validation-app/src/reportWebVitals.js: dynamic import of web-vitals
restaurant-incident-tool/app/dashboard/page.js: useCallback used for fetchIncidents to avoid unnecessary re-creation
Accessibility & Semantics
3/10
Usable for everyone
Basic accessibility practices present (semantic inputs and labels, focus styles), and reduced-motion respected; no evidence of ARIA on custom widgets, focus management beyond defaults, or an a11y test pipeline.
Evidence
restaurant-incident-tool/app/report/page.js: labeled inputs and client-side validation messages
salesforce-validation-app/src/App.css: prefers-reduced-motion rule
State Management & Data Flow
4/10
Managing data in the app
Client-server flow and state are handled deliberately: optimistic UI updates, pendingChanges tracking, input validation, and server-side prepared statements; missing advanced server-state patterns like cancellation, retries with backoff, or optimistic rollback on failures.
Evidence
salesforce-validation-app/src/App.js: pendingChanges object, optimistic toggleRule updates and deployChanges loop
restaurant-incident-tool/app/api/incidents/route.js: input validation and use of prepared statements for DB insertion
UX & Visual Polish
5/10
Look and feel quality
Strong visual polish and UX attention with clear loading/empty/success states, informative messages and transitions, and success flows; UX patterns are consistent and practical for end users.
Evidence
salesforce-validation-app/src/App.js: loading states, message alerts, and empty-state handling for rules
restaurant-incident-tool/app/report/page.js: submit success screen with AI summary and redirect
Expertise
React• Middle
Modern Web Frameworks• Middle
Industries
Food & Beverages• Middle
Technologies
Node JS• Middle
Tailwind CSS
Next.js
Express
React.js
Axios
ESLint
Recommendations
- Build internal admin tools and operational dashboards that integrate with third-party APIs (Salesforce, AI providers) where the developer can own end-to-end frontend and backend integration.
- Develop small-to-medium SaaS features using Next.js App Router and serverless API routes with SQLite/Postgres, focusing on robust server-state patterns and error handling.
- Improve test and CI coverage and add measurable performance benchmarks (LCP, INP) and automated a11y checks to raise reliability and maintainability.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Intern AI/ML Engineer
Confidence: High Data-centric
A data-focused notebook author at an intern level specializing in basic pandas data loading and matplotlib visualization. The strongest proven skill is simple exploratory data handling and plotting as shown by the multiple pd.read_csv calls and plotting cells in the notebook. There is no evidence of model training, experiment tracking, deployment, or production-ready data pipelines in the public code.
Model Architecture & Training
How well models are designed and trained
Not evidenced in public code
Data Pipeline & Feature Engineering
2/10
How data is prepared for models
Basic data loading and exploratory plotting using pandas and matplotlib, limited data transformations and DataFrame use.
Evidence
Pratanudolui/Pandas/Pandas.ipynb: pd.read_csv usage to load multiple stock CSV files (google, facebook, apple, amazon, microsoft)
Pratanudolui/Pandas/Pandas.ipynb: DataFrame creation and df.plot.bar for category value visualization
Pratanudolui/Pandas/Pandas.ipynb: DataFrame column access and plt.plot calls (plt.plot(df['x'], df['y']))
Experimentation & Evaluation
How results are measured and tested
Not evidenced in public code
MLOps & Deployment
How models are shipped to production
Not evidenced in public code
Computational Efficiency
How efficiently computing resources are used
Not evidenced in public code
Research Depth & Innovation
Depth of research and new ideas
Not evidenced in public code
Technologies
Python
SQL• since 2025
MySQL• since 2023
Copilot
Cursor
Claude
Oracle
Jupyter Notebook
Datasets
NumPy
Git• since 2025
SQLite
AWS
Docker
Kubernetes
Gemini
GitHub• since 2025
Machine Learning• since 2024
Recommendations
- Develop simple data cleaning and visualization tasks or internal dashboards that require basic pandas and matplotlib skills.
- Package small ETL scripts or reproducible notebooks that replace absolute local paths with relative paths and provide sample datasets for portability.
- Practice reproducible analysis by adding data validation steps, comments on data types and missing values, and small unit tests for data transforms.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories: