Arun Et-
6+ years exp
Node JS
JavaScript
TypeScript
Python
Active 2 days ago
Invite to interview
Message
Download CVCV
Overview
Technical skills
Roles
Overview
UI engineer (Middle) focused on building component-driven frontends and static site experiences - the strongest proven skill is implementing Gatsby-based static sites and page generation as shown by the gatsby-config.js and gatsby-node.js page creation logic - there is limited public evidence of advanced state management, test coverage, systematic accessibility on custom widgets, or measured performance optimization.
Technical skills
Node JS• Middle
JavaScript• Junior • 6y+
TypeScript• Middle
Python• Middle
Node JS
Express
Mongoose
Bcrypt
Dotenv
Python
FastAPI
HTTPX
Uvicorn
Frontend
React.js
Gatsby
DevOps
AWS
QA
Jest
Middle Backend Developer
Confidence: Medium API Engineer
Backend engineer (Middle) focused on building retrieval-augmented search services and ingestion pipelines for LLM-backed indexing, with a pragmatic strength in integrating vector stores and embedding pipelines.
API Design
3/10
How well APIs are designed
Basic, functional API design is present (FastAPI endpoint, startup initialization, consistent error responses) but lacks versioning, idempotency keys, formal pagination, or a documented error contract.
Evidence
semantic_search_vectorDB/src/main.py: FastAPI app with @app.on_event('startup') and @app.get('/search') endpoint
semantic_search_vectorDB/src/main.py: search(q: str) handler raising HTTPException on uninitialized index
Data Layer & Database
3/10
Working with databases
Uses a vector store (Chroma) with explicit collection checks and loading of VectorStoreIndex, but there is no visible schema evolution, transactional boundaries, or tuned SQL/data-layer optimizations.
Evidence
semantic_search_vectorDB/src/main.py: chromadb.HttpClient usage, chroma_collection.count(), VectorStoreIndex.from_vector_store
semantic_search_vectorDB/src/insurance_query.py: ChromaVectorStore and StorageContext usage to load indices
Scalability & Performance
3/10
Handling load and speed
Shows deliberate batching, device-aware embedding setup and memory management for performance, but lacks system-level decoupling (queues), cache invalidation strategies, or measured load-testing artifacts.
Evidence
semantic_search_vectorDB/src/colab.py: batch processing (BATCH_SIZE = 100), explicit del documents to free memory
semantic_search_vectorDB/src/testing.py: Settings.embed_model device selection and SimpleDirectoryReader load_data(num_workers=4)
System Architecture
3/10
Overall system structure
Reasonable module separation (index setup, query, pdf fetching, EC2 control) and clear startup flow, but no multi-service contracts or advanced decomposition decisions are documented.
Evidence
semantic_search_vectorDB/src/insurance_query.py: start_existing_instance and setup_system separating EC2 control and index setup
semantic_search_vectorDB/src/fetchPdfs.py: independent PDF download/data ingestion utilities
Security & Auth
2/10
Protecting data and access
Some basic input checks and conservative URL handling exist, but security practices are limited (hardcoded IPs/instance ids, printing server responses, no auth on API endpoints, no secrets management shown).
Evidence
semantic_search_vectorDB/src/fetchPdfs.py: URL normalization and filename sanitization in download_pdf/get_filename_from_url
semantic_search_vectorDB/src/main.py: exception branch printing e.response.text and status (reveals remote server message)
Reliability & Observability
2/10
Stability and monitoring
Lightweight reliability measures exist such as logging error files and try/except around batches, but there is no structured logging, retry/backoff policies, timeouts everywhere, or observability/metrics integration visible.
Evidence
semantic_search_vectorDB/src/colab.py: log_error and log_cleaning_anomaly functions writing to files
semantic_search_vectorDB/src/colab.py: try/except around batch processing with log_error and checkpointing
Expertise
Backend AI & LLM• Middle
Databases & Vector Storage• Middle
Microservices & API Architecture• Middle
Industries
Financial Services• Middle
Technologies
Python• Middle
FastAPI
AWS
HTTPX
Uvicorn
Django• mentioned only
Recommendations
- Develop production-grade reliability: add structured logging, metrics (Prometheus/OpenTelemetry), timeouts and retries with jitter, and graceful shutdown in the FastAPI service.
- Harden security and secrets handling: remove hardcoded IPs/instance ids, use environment-backed secrets, avoid printing remote server error bodies, and add authentication/authorization for the API.
- Introduce operational contracts and observability for the vector store: add health endpoints, background reindexing with idempotency, and clear collection/versioning strategy for schema/evolution.
- Separate ingestion into a queue-backed pipeline (e.g., Celery/RQ or AWS SQS) to decouple downloads, parsing, and embedding to improve scalability and failure isolation.
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
UI engineer (Middle) focused on building component-driven frontends and static site experiences - the strongest proven skill is implementing Gatsby-based static sites and page generation as shown by the gatsby-config.js and gatsby-node.js page creation logic - there is limited public evidence of advanced state management, test coverage, systematic accessibility on custom widgets, or measured performance optimization.
UI Component Architecture
3/10
How interface parts are built
Reasonable component-level work and CSS-driven UI composition is present but there is little evidence of a deliberate component system, tokens, or custom hook architecture for large applications.
Responsive & Cross-browser
3/10
Works on all screens and browsers
Basic responsive and cross-browser concerns are addressed via media queries, normalize.css and vendor fallbacks, but there is no advanced layout strategy or explicit RTL/i18n or feature-detection work.
Performance Optimization
2/10
Speed of the interface
Some awareness of build and analysis tooling exists (Gatsby bundle analyzer entry and modern dev scripts) but there is no demonstrated measured performance work, bundle analysis artifacts, or targeted optimizations in the code.
Accessibility & Semantics
2/10
Usable for everyone
Semantic HTML and GraphQL-driven meta schema types are used which helps accessibility, but there is little evidence of explicit a11y on custom widgets, focus management, keyboard handlers or CI a11y checks.
State Management & Data Flow
2/10
Managing data in the app
Data flow is mostly simple page-level GraphQL queries or small local state in interactive scripts; there is no evidence of advanced server-state discipline, request cancellation, optimistic updates or state machines.
UX & Visual Polish
3/10
Look and feel quality
Good visual polish on small interactive parts and animations is present, with UX feedback and simple transitions, but there are few systematic UX patterns like skeletons, undo or perceived-performance strategies.
Verified artifacts
Expertise
React• Middle
HTML & CSS• Middle
Frontend Architecture & Build Tools• Middle
Technologies
TypeScript• Middle
Node JS• Middle
Express
Bcrypt
React.js
Mongoose
Dotenv
Gatsby
Recommendations
- Develop component libraries and accessible UI primitives for dashboard or admin interfaces using React and design tokens.
- Build and maintain Gatsby-based marketing sites and blogs with proper SEO and GraphQL-driven content pipelines.
- Prototype small micro-frontend integrations and experiment with module federation for independent deployable UI slices.
- Create interactive HTML5/Canvas mini-games or audio-visual experiences where polished animations and UX micro-interactions matter.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Junior QA Engineer
Confidence: Medium Generalist
Frontend JavaScript developer (Junior) specializing in React-based single-page apps with Firebase integration. The strongest proven skill is building client-side auth and realtime database flows as shown in src/containers/auth/firebase-config.js and src/containers/auth/index.js. There is little to no evidence of automated testing, CI test engineering, backend integration beyond Firebase, or QA/test infrastructure in the public human-authored code.
Test Automation Frameworks
Building automated tests
Not evidenced in public code
Test Coverage & Strategy
What and how to test
Not evidenced in public code
API & Integration Testing
Testing how parts work together
Not evidenced in public code
Performance & Load Testing
Testing speed under load
Not evidenced in public code
Bug Reporting & Analysis
Finding and describing bugs
Not evidenced in public code
CI Test Integration
Running tests automatically
Not evidenced in public code
Technologies
JavaScript• Junior • 6y+
Jest
Django• mentioned only
Recommendations
- Develop interactive React single-page applications with Firebase authentication and realtime database features.
- Implement and maintain small-to-medium UI features using styled-components and component-driven design.
- Build client-side prototypes and feature demos that require quick user-auth flows and CRUD operations with Firebase.
- Expand into adding unit and component tests (React Testing Library / Jest) and basic CI test pipelines for stability.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories: