Dhruv Kabariya

Mobile • Junior • Bengaluru, India • UTC+05:30 • Work permits: India • Available now
Software Developer
5+ years exp
Python
Go
C
Dart
Active 15 days ago
+91 (8000) 014062
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Overview
Technical skills
Timeline
Roles

Overview

A junior cross-platform mobile developer focused on Flutter who implements UI and state using the BLoC pattern. The strongest proven skill is BLoC-based state management and UI composition as seen across the bloc folders and human-authored screens (for example lib/screen/detailview.dart and the various lib/*/bloc/*.dart files). There is little to no evidence of lifecycle/process-death handling, offline sync, background work, performance measurement, automated tests or release/CI automation in the human-authored code.

Technical skills

Python
Go
C
Dart• Junior • 5y+
Python
FastAPI
C
FFmpeg
Dart
GetX
Bloc
Databases
Apache Kafka
OpenSearch
Redis
ElasticSearch
AI/ML
Embeddings
YOLO
PyTorch
Spark
Pandas
NumPy
Google Colab
Vision
Mobile
Flutter
Declarative UI
Material Design
State Management
Repository Pattern
DevOps
Docker
GCP
Grafana
gRPC
Kubernetes
Prometheus
Vector
Rest API

Timeline

Ahmedabad University
Bachelor's Degree • Computer Science Engineering
2019–2023 Ahmedabad, Gujarat
Research Intern • Junior
Ahmedabad University • Full-Time
Ahmedabad In office
Worked on scalable web-scraping pipelines to generate large annotated datasets for research experiments. Fine-tuned models and evaluated OCR and text-recognition approaches by analyzing performance trade-offs to improve inference quality. Produced experiments to support research-grade dataset and model quality.
Lead Flutter Developer • Lead
Jolly Hires • Full-Time
Ahmedabad In office
Led a Flutter engineering effort including feature planning, code reviews, and quality-focused delivery. Migrated an existing Flutter architecture from GetX to BLoC, reducing state-related bugs and improving scalability. Supported app deployment and ongoing maintenance to ensure compliance requirements were met.
GetX
Software Development Engineer • Middle
6thlabs • Full-Time
Ahmedabad In office
Developed Go microservices using gRPC to support real-time location broadcasting for large concurrent audiences. Built a real-time speech-to-text pipeline with custom Wav2Vec running on GCP GPU and focused on reducing transcription latency. Implemented YOLO-based traffic-violation detection and created multi-camera livestream ingestion using Python multiprocessing for high-throughput processing.
Go
gRPC
Apache Kafka
GCP
YOLO
Python
Software Development Engineer 2 (Backend) • Middle
Loco • Full-Time
Bengaluru In office
Improved the Feed API by reducing p95 latency through diagnosis and optimization of Redis sorted-set bottlenecks. Re-architected an HLS-to-MP4 video processing pipeline by enforcing FFmpeg CPU limits to stop pod crashes and reduce processing failures. Built scalable leaderboard and scoring pipelines, including winner computation and rewards allocation, and supported high concurrency. Secured the CloudFront + IVS playback API to prevent misuse.
Redis
FFmpeg
Kubernetes
Junior Mobile Developer Confidence: Medium Cross-platform
A junior cross-platform mobile developer focused on Flutter who implements UI and state using the BLoC pattern. The strongest proven skill is BLoC-based state management and UI composition as seen across the bloc folders and human-authored screens (for example lib/screen/detailview.dart and the various lib/*/bloc/*.dart files). There is little to no evidence of lifecycle/process-death handling, offline sync, background work, performance measurement, automated tests or release/CI automation in the human-authored code.
Platform Native Mastery
1/10
Knowing the mobile platform
Minimal platform-native lifecycle handling - UI code uses regular widgets and controllers but shows no explicit process-death/state-restoration, restorationId, or platform lifecycle/cancellation handling tied to screens.
Evidence
lib/screen/detailview.dart: widgets and TextEditingControllers are used without state restoration or restorationId patterns
lib/screen/searchField.dart: TextEditingController instances are created and used in stateless widgets without restoration/cancellation logic
Mobile UI/UX & Responsiveness
2/10
Smooth mobile experience
Basic, conventional Flutter UI - uses ListView, ExpansionTile and standard layout patterns but relies on fixed widths/heights and does not show adaptive layouts, dynamic type or explicit responsiveness for different device classes.
Evidence
lib/screen/detailview.dart: uses fixed widths/heights (e.g. width: 350, height: 600) for major UI containers
lib/screen/searchField.dart: search input and result cards sized for a narrow layout rather than responsive breakpoints
Performance & Battery
1/10
Speed and battery use
No measurable performance/battery work - no baseline profiles, no caching strategies, and some synchronous image handling that could be heavy on memory and CPU.
Evidence
lib/screen/reviewpost.dart: picked image is base64-encoded synchronously (base64Encode(result.readAsBytesSync())) which can be expensive on large images
lib/screen/detailview.dart: Image.network used inside a horizontal ListView with no caching/placeholder strategy
Offline & Data Sync
1/10
Working offline and syncing
No offline-first or robust sync engine is present - network interactions appear to be immediate via blocs without local persistence, queueing or idempotent retry strategies.
Evidence
lib/screen/reviewpost.dart: posts review events to DetailBloc with no local queueing or retry mechanism
lib/screen/detailview.dart: data consumption via DetailBloc but no local DB usage or migration patterns are visible in human-authored UI code
Device Integration
2/10
Using device features
Some device/desktop integration evidence (file picking on Windows), but mobile permission flows and full device-edge handling are not present.
Evidence
lib/screen/reviewpost.dart: uses filepicker_windows.OpenFilePicker to pick images and then base64-encodes them
pubspec.yaml: declares dependency filepicker_windows, indicating explicit integration with a platform file picker
Release & App Lifecycle
1/10
Building and publishing apps
No release engineering artifacts or crash/analytics wiring found in the human-authored UI code; build files in the repo appear default/boilerplate and there is no CI/fastlane/release automation evidence.
Evidence
lib/screen/detailview.dart: uses print() for debug and contains no crash-reporting or analytics integrations
lib/screen/reviewpost.dart: debug prints (print(result.path), print(user.username)) instead of structured logging or crash hooks
Expertise
Flutter• Junior
Industries
Education• Junior
Technologies
Dart• Junior • 5y+
Flutter
Bloc
Repository Pattern
Declarative UI
State Management
Material Design
Recommendations
  • Assign small-to-medium Flutter feature work: UI screens, BLoC flows and API integration (search, detail, review posting) where the developer can extend existing blocs and services.
  • Have the developer add offline resilience and syncing: implement a local persistence layer (e.g., Drift/SQLite or Hive), queued idempotent requests and retry logic integrated with existing blocs.
  • Ask the developer to harden image handling and performance: move image encoding to isolates, add caching (cached_network_image), and add simple performance measurements (frame_build_times, memory) and targeted optimizations.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
aumap
Intern AI/ML Engineer Confidence: Low Generalist
A generalist at an entry-level (intern/junior) capability with almost no human-authored production code available for technical assessment. The strongest proven signal is a basic data project description for house price prediction using Spark-based tooling as described in project documentation. There is no human-authored model, training loop, evaluation pipeline, or deployment code present to validate applied engineering skills.
Model Architecture & Training
How well models are designed and trained
Not evidenced in public code
Data Pipeline & Feature Engineering
How data is prepared for models
Not evidenced in public code
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
Industries
Data & Analytics• Intern
Real Estate• Intern
Technologies
Vision
Python
Redis
Rest API
gRPC
Spark
GCP
YOLO
FastAPI
Prometheus
Embeddings
Google Colab
Pandas
NumPy
PyTorch
Docker
Kubernetes
ElasticSearch
Apache Kafka
Grafana
OpenSearch
Vector
Recommendations
  • Develop and maintain PySpark-based ETL and regression pipelines for tabular/real-estate data, focusing on reproducible notebooks and clear data schemas.
  • Prototype simple computer-vision demos using established frameworks and provide concise notebooks showing training and evaluation (e.g., object detection using off-the-shelf models).
  • Implement Kafka-based ingestion pipelines and small Go or Python microservices for streaming data, with basic tests and deployment instructions.
  • Improve public engineering signal by adding human-authored code: clear scripts, unit tests, experiment logs, and small reproducible training/evaluation runs.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
BDA-Project dhruv-kabariya
Intern Blockchain Developer Confidence: Low
An early-stage developer at an intern level with no verifiable human-authored source code available for technical evaluation. No proven engineering skills are evidenced in public code because there are no human-authored source files available for analysis. There is no public evidence of smart contract development, security/audit artifacts, protocol engineering, or DApp integration work.
Smart Contract Development
Writing smart contracts
Not evidenced in public code
Security & Audit
Checking contracts for flaws
Not evidenced in public code
Blockchain Protocol Understanding
Knowing how blockchains work
Not evidenced in public code
DApp & Web3 Integration
Connecting apps to blockchain
Not evidenced in public code
Tokenomics & DeFi Logic
Token and finance logic
Not evidenced in public code
Decentralization & Trust Model
Designing trust without middlemen
Not evidenced in public code
Recommendations
  • Author and publish a small, human-authored smart contract (for example a minimal music-rights registry or ERC-721/erc20 example) with clear natspec and a concise README describing trust assumptions.
  • Add unit tests and invariant/negative tests (Foundry or Hardhat), include at least one attacker-style test or fuzz case, and enable CI to run the tests automatically.
  • Create a simple frontend demonstrating wallet integration (ethers.js or web3.js) and emit events in contracts with a short event-indexing plan (subgraph or custom indexer).
  • Add repository metadata that maps to code ownership - LICENSE, CONTRIBUTING, CODE_OF_CONDUCT, and ensure meaningful commit attribution so file ownership is clear.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
dusic