Shraddha Torkad
Python Developer
4+ years exp
Python
SQL
JavaScript
Node JS
C++
C
PHP
API Design: 5/10
Data Layer & Database: 4/10
System Architecture: 4/10
Active 6 hours ago
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Overview
Technical skills
Projects
Timeline
Roles
Overview
Backend-focused Python engineer (middle-level) with a clear strength in designing and testing domain logic for financial features. The strongest proven skill is precise EMI and amortization logic implemented and unit-tested in backend/services/emi_service.py and backend/tests/test_emi.py. There is limited evidence of production-grade security, secret management, resilience patterns and large-scale system design in public code.
Phone
Technical skills
Languages
7
Python
SQL
JavaScript
Node JS
C++
C
PHP
Python
4
Django
FastAPI
Pydantic
SQLAlchemy
AI/ML
5
NumPy
Pandas
Scikit-learn
TensorFlow
LLM
Frontend
3
React.js
Tailwind CSS
React Router
Other
30
Matplotlib
Seaborn
Axios
AWS
PostgreSQL
Django REST Framework
Flask
OpenCV
MySQL
Redis
TensorFlow C++
Alembic
Power BI
Vite
GitHub
GitHub Actions
Uvicorn
PostCSS
npm
Git
Rest API
Docker
Postman
AI Agents
Machine Learning
CI/CD
RAG
autoprefixer
NLP
ETL/ELT
Projects
Data Visualization & Reporting Projects
Develops interactive Power BI dashboards and Python-based reports for trend analysis and KPI monitoring. Transforms raw datasets into charts and executive-ready summaries. The goal is to make performance metrics easier to interpret for decision-making.
QR Code Attendance Analytics System
Creates a web application for QR code-based attendance tracking with structured data management. Generates Excel reports automatically and performs attendance trend analysis to support administrative decisions. Emphasis is on reliable data flow from attendance events to reporting and insights.
ATS Resume Screening Analytics
Builds an automated resume screening system that applies NLP to extract skills from resumes and scores candidates against predefined criteria. Adds analytics dashboards to support candidate ranking and faster, more consistent shortlisting. The system focuses on turning unstructured resume text into structured, decision-ready outputs.
Timeline
Python Developer
•
Middle
onestaff.ai
•
Full-Time
Develops and maintains REST APIs for an AI-driven HR platform, supporting recruitment and onboarding workflows with role-based access. Manages multi-tenant PostgreSQL schemas using SQLAlchemy and Alembic migrations, and improves backend performance by optimizing queries and logic. Builds automated CRM workflows and asynchronous email notifications using a Redis-backed worker, and integrates LLM-powered in-app features with usage tracking. Ships updates via Docker-based environments with CI-gated pull requests and automated checks.
Python
SQLAlchemy
PostgreSQL
Alembic
Redis
PHP
MySQL
LLM
Docker
CI/CD
Data Analytics & AI/ML Intern
•
Junior
Vareli Tecnac Pvt. Ltd.
•
Internship
Cleans, preprocesses, and transforms structured datasets using Python with Pandas and NumPy to improve data integrity. Conducts exploratory data analysis to identify trends and produce actionable insights. Builds and tunes machine learning models with Scikit-learn and TensorFlow, and presents results through visual reports and stakeholder-ready dashboards using Matplotlib and Seaborn.
Python
Pandas
NumPy
Machine Learning
Scikit-learn
TensorFlow
Matplotlib
Seaborn
Python Django Developer Intern
•
Junior
SSIT Pvt. Ltd.
•
Internship
Develops and maintains web application features using Python and Django with an MVC-based approach. Designs relational database schemas and optimizes SQL queries for efficient data handling. Implements RESTful APIs and backend business logic, and supports QA testing and production deployments to keep the application stable.
Python
Django
SQL
Middle Backend Developer
Confidence: Medium API Engineer
Backend-focused Python engineer (middle-level) with a clear strength in designing and testing domain logic for financial features. The strongest proven skill is precise EMI and amortization logic implemented and unit-tested in backend/services/emi_service.py and backend/tests/test_emi.py. There is limited evidence of production-grade security, secret management, resilience patterns and large-scale system design in public code.
API Design
5/10
How well APIs are designed
Clear REST API structure with FastAPI routers and Pydantic schemas and some consistent error handling, but missing versioning, pagination, idempotency strategy and formal error contract.
Evidence
loan-emi-tracker/backend/routers/loans.py: APIRouter endpoints with response_model and HTTPException usage
loan-emi-tracker/backend/schemas/schemas.py: Pydantic models and field_validator input validation
loan-emi-tracker/frontend/src/services/api.js: axios client with auth interceptor and 401 response handling
Data Layer & Database
4/10
Working with databases
Uses ORM models and migrations and demonstrates transactional commits and explicit flush/refresh, but lacks explicit transaction boundaries, rollback handling and hand-tuned queries or index reasoning.
Evidence
loan-emi-tracker/backend/routers/loans.py: db.flush(), db.commit(), db.refresh() around schedule persistence
QR-attendence/attendance_system/core/models.py: Django ORM model definitions with relations
QR-attendence/attendance_system/core/migrations/0001_initial.py: migration history for schema
Scalability & Performance
2/10
Handling load and speed
Minor performance-conscious code (select_related used) and reasonable client-side behavior, but no caching strategy, queueing, rate limiting or measured optimizations.
Evidence
QR-attendence/attendance_system/core/views.py: AttendanceRecord.objects.select_related(...) to avoid N+1
loan-emi-tracker/frontend/src/services/api.js: axios client usage but no caching or retry policies
System Architecture
4/10
Overall system structure
Reasonable module separation into routers, services and schemas and a clear frontend-backend boundary, but overall a monolith with minimal config/secret management and no documented service decomposition trade-offs.
Evidence
loan-emi-tracker/backend/services/emi_service.py: domain service encapsulating EMI logic
loan-emi-tracker/backend/routers/loans.py: router layer for API endpoints
loan-emi-tracker/frontend/src/services/api.js: separate frontend API client module
Security & Auth
2/10
Protecting data and access
Basic input validation is present, and frontend handles 401s, but authentication is simplistic (hardcoded) and there is limited evidence of token lifecycle, secrets hygiene or threat mitigation.
Evidence
loan-emi-tracker/backend/utils/auth.py: hardcoded credentials and simple token invalidation
loan-emi-tracker/backend/schemas/schemas.py: validators for principal, tenure and salary
loan-emi-tracker/frontend/src/services/api.js: Authorization header injection from localStorage and 401 handler
Reliability & Observability
3/10
Stability and monitoring
There is unit testing for core financial logic and some logging, but the codebase lacks systematic retries, backoff, timeouts, graceful shutdowns and structured observability artifacts.
Evidence
loan-emi-tracker/backend/tests/test_emi.py: unit tests covering EMI calculations and edge cases
QR-attendence/attendance_system/core/views.py: logger usage and guarded exception handling
loan-emi-tracker/frontend/src/services/api.js: response interceptor handling auth failures
Expertise
Python• Middle
Databases & Vector Storage• Middle
Microservices & API Architecture• Middle
Industries
Education• Middle
Financial Services• Middle
Technologies
Python• since 2022 • Middle
PHP• since 2026
Node JS• Middle
Rest API
Flask
SQLAlchemy• since 2026
FastAPI
Django• since 2022
Pydantic
Uvicorn
Axios
Stack• mentioned only
Recommendations
- Develop and harden REST APIs and business logic for financial features - implement production-grade auth, token lifecycle and audit trails for loan operations.
- Extend the EMI service into a small bounded service with integration tests and database migrations - add transactional safeguards and rollback semantics around schedule persistence.
- Improve security and ops posture - remove hardcoded secrets, add secrets/config management, enforce secure ALLOWED_HOSTS and CSRF settings, and add structured logging and metrics.
- Implement caching, pagination and idempotency for API endpoints that return lists or perform mutating operations to improve scalability and client resilience.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Middle Frontend Developer
Confidence: Medium App Engineer
A middle-level frontend app engineer specializing in practical React application wiring and API integration. The strongest proven skill is API integration and server-state discipline, demonstrated by src/services/api.js which centralizes an axios client with interceptors, token handling, error extraction and per-endpoint wrappers. There is limited evidence of advanced state management, formal testing, request cancellation or measured performance and accessibility work beyond basic focus styles.
UI Component Architecture
2/10
How interface parts are built
Basic component and styling scaffolding is present but there are no bespoke React component files, composition patterns, or a custom component library; evidence is limited to Tailwind tokens and CSS utility classes.
Responsive & Cross-browser
3/10
Works on all screens and browsers
Responsive layout is driven by Tailwind and sensible theme tokens; there is basic focus styling and font preconnects but no advanced responsive techniques, RTL, or feature detection.
Performance Optimization
2/10
Speed of the interface
No measured performance work or bundle analysis is present; only minimal build-tool usage (Vite) and font preconnect optimizations are visible.
Accessibility & Semantics
3/10
Usable for everyone
Some a11y awareness in base CSS (focus-visible outline and reduced motion not evident) and semantic defaults, but no ARIA on custom widgets, keyboard management, or automated a11y checks.
State Management & Data Flow
4/10
Managing data in the app
Clear, pragmatic server-state discipline in a single API module: a centralized axios client with request/response interceptors, token management, consistent error extraction, and a set of typed API wrapper functions; however there is no advanced request cancellation, optimistic updates or state machines.
Evidence
shraddha891/loan-emi-tracker/frontend/src/services/api.js: axios client with request interceptor for token and response interceptor handling 401
shraddha891/loan-emi-tracker/frontend/src/services/api.js: extractErrorMessage and per-endpoint wrapper functions (fetchLoans, createLoan, payEmi, downloadOutstandingCsv)
UX & Visual Polish
3/10
Look and feel quality
Visual polish is present via design tokens, color system and reusable utility classes for cards, buttons and tables, but UX edge states like skeletons, rich loading/error/empty handling and undo patterns are not broadly evidenced.
Expertise
React• Middle
Frontend Architecture & Build Tools• Middle
Industries
Financial Services• Middle
Technologies
JavaScript• Middle
Tailwind CSS
React.js
Vite
npm
PostCSS
React Router
autoprefixer
Recommendations
- Develop admin dashboards and reporting front-ends that rely on REST APIs and CSV exports, where the existing API client patterns are a direct fit.
- Implement medium-complexity loan or finance workflows including form validation, optimistic UI for payments and guarded foreclosures to expand server-state handling expertise.
- Harden the frontend by adding request cancellation, optimistic update rollbacks, automated a11y checks and end-to-end tests to raise reliability for production use.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Junior AI/ML Engineer
Confidence: Medium Data-centric
Data-centric developer at a junior level specializing in exploratory data analysis and lightweight data-driven web tools. Strongest proven skill is data cleaning and exploratory analysis demonstrated by helper cleaning functions (clean_rate, clean_cost, clean_price), summary statistics, and multiple saved visualizations across the analysis scripts. There is little to no evidence of model training, experiment tracking, automated testing, or production-grade deployment practices in the public code.
Model Architecture & Training
How well models are designed and trained
Not evidenced in public code
Data Pipeline & Feature Engineering
3/10
How data is prepared for models
Basic data cleaning and feature extraction using pandas and numpy; explicit handling of missing values and type conversions in helper functions.
Evidence
shraddha891/data-analysis-portfolio/zomato_analysis.py: clean_rate, clean_cost
shraddha891/data-analysis-portfolio/flipkart_analysis.py: clean_price and main_category extraction
shraddha891/data-analysis-portfolio/ipl_analysis_fixed (1).py: matches['winner'].fillna(...) and grouping/aggregation logic
Experimentation & Evaluation
1/10
How results are measured and tested
Minimal experimentation or evaluation artifacts; summary statistics and saved charts are present but no reproducible experiment tracking or validation pipelines.
MLOps & Deployment
2/10
How models are shipped to production
Basic deployment-level code in the form of a simple Flask app for resume upload and keyword scoring; suitable for prototypes but lacking production concerns like auth, sanitization, versioning, or monitoring.
Computational Efficiency
1/10
How efficiently computing resources are used
No evidence of GPU/CPU profiling, batching, quantization or other efficiency engineering; a few sampling calls are used to limit plot sizes.
Research Depth & Innovation
Depth of research and new ideas
Not evidenced in public code
Expertise
Document Intelligence & OCR• Junior
Industries
Commerce• Junior
Professional Services• Junior
Technologies
SQL• since 2022
C++
MySQL• since 2026
PostgreSQL• since 2026
Redis• since 2026
OpenCV• since 2026
GitHub Actions
Scikit-learn• since 2025
AI Agents
NLP• since 2026
CI/CD• since 2026
TensorFlow• since 2025
Pandas• since 2025
NumPy• since 2025
Git
AWS
Docker• since 2026
LLM• since 2026
RAG• since 2026
TensorFlow C++
Django REST Framework
Alembic• since 2026
GitHub
Machine Learning• since 2025
Recommendations
- Develop end-to-end EDA reports and visualizations for e-commerce and consumer datasets, including reproducible scripts that output both figures and CSV summaries.
- Build small data-cleaning libraries or modules (with unit tests) that centralize parsing/cleaning logic (e.g., price, rating, cost parsers) to improve reuse and maintainability.
- Extend the Flask ATS prototype toward a secure proof-of-concept: add filename sanitization, authentication, input validation, and a basic persistence layer for uploads and results.
- Implement lightweight experiment tracking and reproducibility (e.g., save random seeds, sample sizes, and a requirements file) and add a few unit tests for cleaning functions.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories: