Adithya Sumanth
React Native Developer
4+ years exp
JavaScript
TypeScript
State Management & Data Flow: 5/10
UI Component Architecture: 4/10
Performance Optimization: 4/10
Active 2 days ago
+91 (9618) 046993
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Overview
Technical skills
Timeline
Roles
Overview
Mobile app engineer (senior-level) focused on offline-first React Native applications with reliable sync and native integration as the core strength. The strongest proven skill is designing client-side data and sync boundaries, evidenced by token/keychain handling, token rotation provider, and dedicated feature packages such as feature-auth and feature-jobs. There is limited publicly credited evidence of accessibility engineering, measured performance work, or UI component composition source files in the human-authored set, and several core algorithm files were flagged as AI-authored and excluded from scoring.
Technical skills
JavaScript
TypeScript• Middle • 4y+
Databases
SQLite
Frontend
Redux
Socket.IO
React.js
Zustand
i18next
Redux Toolkit• 4y+
React Query
DevOps
CI/CD
Git
GitHub Actions
Rest API
WebSockets
Mobile
Deep Linking
Expo
State Management
React Native• 4y+
Crashlytics
Firebase
Offline-First
Realm
Clean Architecture
Timeline
Software Engineer
•
Middle
GeekyAnts India Pvt Ltd
•
Full-Time
Developed and optimized React Native features with focus on improving runtime performance and reducing UI pauses during heavy list rendering. Used Hermes profiling and memoization to identify and remove JavaScript thread bottlenecks. Built Turbo Modules for faster native-to-JS communication, and applied TanStack Query to reduce redundant network calls and stabilize UI on low-bandwidth connections.
React Native
React Query
Clean Architecture
Member of Technical Staff (Mobile)
•
Lead
Kshema General Insurance Ltd
•
Full-Time
Built production Android and iOS mobile applications with offline-first workflows for field agents. Integrated Realm Database with background synchronization so users can collect data offline and sync automatically when connectivity returns. Resolved memory leaks and UI jank using profiling tools, and integrated core features such as biometrics, push notifications, and geolocation into insurance workflows.
React Native
Offline-First
Realm
Mobile Developer
•
Middle
Unsolved Network
•
Full-Time
Maintained consumer-facing React Native applications and owned feature delivery, native integrations, and app release preparation. Improved feed performance by redesigning list rendering and optimizing image handling to reduce memory usage on low-end devices. Implemented real-time messaging behavior using Socket.io-style WebSocket communication and monitored production crashes using Firebase Crashlytics.
React Native
WebSockets
Crashlytics
Firebase
Assistant System Engineer
•
Middle
Empover I-Tech Pvt Ltd
•
Full-Time
Developed enterprise mobile solutions using React Native with Redux Toolkit and TypeScript. Created reusable UI components and form modules to standardize application layout and speed up delivery across teams. Implemented secure storage for authentication tokens and integrated payment workflows with robust error handling.
React Nativesince 2022
Redux Toolkit
TypeScript
Jawaharlal Nehru Technological University Hyderabad (JNTUH)
Bachelor's Degree •
Electronics
Jawaharlal Nehru Technological University Hyderabad (JNTUH)
High School Diploma •
Electronics
Middle Frontend Developer
Confidence: Medium App Engineer
Mobile app engineer (senior-level) focused on offline-first React Native applications with reliable sync and native integration as the core strength. The strongest proven skill is designing client-side data and sync boundaries, evidenced by token/keychain handling, token rotation provider, and dedicated feature packages such as feature-auth and feature-jobs. There is limited publicly credited evidence of accessibility engineering, measured performance work, or UI component composition source files in the human-authored set, and several core algorithm files were flagged as AI-authored and excluded from scoring.
UI Component Architecture
4/10
How interface parts are built
Deliberate package boundaries and a shared ui-kit indicate component thinking, but there is limited visible custom component implementation or composition patterns in the analyzed human-authored files.
Evidence
packages/ui-kit/package.json
apps/mobile/package.json
packages/feature-jobs/package.json
Responsive & Cross-browser
3/10
Works on all screens and browsers
Cross-platform and brownfield host integration details are present, showing practical cross-device considerations, but there is little evidence of advanced responsive strategies or RTL/i18n runtime handling in UI code.
Evidence
apps/native-host-ios/README.md
apps/native-host-android/README.md
apps/mobile/package.json
Performance Optimization
4/10
Speed of the interface
Concrete performance-conscious choices are visible through dependencies and build checks (virtualized list, bundle budget scripts, E2E verification), though no measured before/after metrics or bundle-analyzer artifacts are present.
Evidence
apps/mobile/package.json
package.json
apps/mobile/e2e/criticalPath.e2e.ts
Accessibility & Semantics
2/10
Usable for everyone
Basic internationalization tooling is present, but there is minimal direct evidence of accessibility work such as ARIA on custom widgets, keyboard focus management, or CI a11y checks.
Evidence
apps/mobile/package.json
packages/ui-kit/package.json
State Management & Data Flow
5/10
Managing data in the app
There is clear evidence of thought around client state and offline flows - token/keychain handling, token rotation, and feature packages wired with server-state libraries - but the deepest sync/conflict algorithm files were flagged as AI-authored and therefore not credited directly here.
Evidence
packages/feature-auth/src/keychain.ts
apps/mobile/src/providers/tokenRotation.ts
packages/feature-jobs/package.json
UX & Visual Polish
3/10
Look and feel quality
UX discipline is evident via E2E critical-path tests and a shared design token library, but explicit UX polish artifacts like skeletons, undo patterns, or nuanced empty/stale state handling are not visible in the human-authored set.
Evidence
apps/mobile/e2e/criticalPath.e2e.ts
packages/ui-kit/package.json
apps/mobile/README.md
Expertise
React• Middle
Industries
Commerce• Middle
Transportation & Logistics• Middle
Technologies
JavaScript
TypeScript• Middle • 4y+
Rest API
Zustand
Redux
Socket.IO
GitHub Actions
WebSockets
CI/CD
Git
React.js
React Query
i18next
Redux Toolkit• 4y+
Systems• mentioned only
Turborepo• mentioned only
Recommendations
- Lead development of offline-first mobile features and sync engines that require robust outbox and retry semantics.
- Implement and harden native module integrations or TurboModule scanners for iOS and Android hosts.
- Own end-to-end testing and CI gating for mobile critical paths, including Detox-based flows and bundle budget enforcement.
- Develop and evolve a React Native design system and component library with explicit accessibility and performance metrics.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Intern Backend Developer
Confidence: Medium Generalist
Python developer focused on LLM-driven data automation at a junior level with a strength in quickly prototyping LLM-assisted data analysis workflows. The strongest proven skill is integrating OpenAI ChatCompletion flows and orchestrating end-to-end generation-execution-analysis in main.py and demo/demo_main.py. There is little evidence of production backend engineering, secure execution sandboxing, database schema evolution or service-level resilience in public code.
API Design
2/10
How well APIs are designed
Basic API usage is present (OpenAI and SerpApi calls) but there is no API design, versioning, error contract, or idempotency strategy.
Evidence
GPT_ANALYST/main.py: get_gpt_result uses openai.ChatCompletion.create(model='gpt-4', ...)
GPT_ANALYST/demo/demo_main.py: get_gpt_result uses openai.ChatCompletion.create(model='gpt-4', ...)
Data Layer & Database
1/10
Working with databases
Lightweight local data handling with SQLite files and schema parsing exists, but no migrations, transactions, tuned queries or integrity management are implemented.
Evidence
GPT_ANALYST/main.py: copies and opens sqlite file (shutil.copyfile('/nvBench-main/database/'+sql+'/'+sql+'.sqlite', sql+'.sqlite')) and reads schema.sql
GPT_ANALYST/demo/demo_main.py: reads schema file and passes schema to model-generated code (schema = extract_create_table(schema_file))
Scalability & Performance
1/10
Handling load and speed
No scalability or performance engineering - scripts run sequentially, no caching, no queueing, no connection pooling or load testing artifacts.
Evidence
GPT_ANALYST/main.py: sequential Step1/Step2/Step3 flow with time measurements but no pooling or async workers
GPT_ANALYST/demo/demo_main.py: sequential execution and os.system('python demo.py') without sandboxing or queuing
System Architecture
1/10
Overall system structure
Monolithic scripts with top-level procedural flow; no deliberate service decomposition, config management, or graceful degradation strategies.
Evidence
GPT_ANALYST/main.py: single-file script with if __name__ == '__main__' driving the whole pipeline
GPT_ANALYST/demo/demo_main.py: monolithic CLI + UI flow (tkinter) in a single file
Security & Auth
1/10
Protecting data and access
API keys are read from env or prompted, but executing model-generated code without sandboxing and widespread bare excepts show weak input/supply-chain security and error handling.
Evidence
GPT_ANALYST/main.py: openai.api_key = os.environ['OPENAI_API_KEY'] and later os.system('python demo.py') executes untrusted generated code
GPT_ANALYST/demo/demo_main.py: prompts user for API keys (input) and assigns google_key variable used directly in query_google
Reliability & Observability
1/10
Stability and monitoring
Minimal observability and reliability: plain prints and elapsed time, but no structured logging, no retries/backoff beyond ad-hoc, and many broad except blocks that swallow errors.
Evidence
GPT_ANALYST/main.py: prints step times and uses broad try/except blocks that swallow exceptions (multiple 'except:' with no handling)
GPT_ANALYST/demo/demo_main.py: similar printed timings and broad exception handling, no metrics or structured logs
Expertise
Backend AI & LLM• Intern
Technologies
SQLite
Stack• mentioned only
Systems• mentioned only
Recommendations
- Develop LLM-assisted data tooling and prototypes where quick iteration is primary, for example CLI tools that generate and run short analyses against local SQLite datasets.
- Build a safe sandbox to execute model-generated code (containerized runner or AST-based validator) before running generated scripts with os.system.
- Harden automation by replacing broad excepts with explicit error handling, add structured logging and basic metrics, and validate presence of API keys and inputs.
- If moving to production backends, implement migration history for databases, transaction boundaries, and secure secret management (env/keystore) rather than interactive input prompts.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories: