Riddhi Das
ML Engineer
C++
Python
SQL
C
UX & Visual Polish: 6/10
Performance Optimization: 5/10
UI Component Architecture: 4/10
Active 6 days ago
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Overview
Technical skills
Timeline
Roles
Overview
UI-focused Frontend Engineer (early-career, middle-level) with a strong eye for polished interactions and visual design. The strongest proven skill is client-side UX engineering and performant animations as shown by the requestAnimationFrame particle system, IntersectionObserver-driven reveal/skill animations and the gallery/modal code in script.js. There is little evidence of framework-based component architecture, automated tests, server-state handling, or accessibility focus management for complex widgets.
Technical skills
C++
Python
SQL
C
Databases
FAISS
MySQL
PostgreSQL
Snowflake
Oracle
AI/ML
AI Agents
Anomaly Detection
Copilot
Groq
Jupyter Notebook
LangChain
LangGraph
NumPy
Ollama
Pandas
RAG
SHAP
XGBoost
Gemini
AI/ML
NLP
Frontend
Frontend
DevOps
Amazon EC2
AWS
GCP
Git
Robotics
Digital Twin
Timeline
Brainware University
Bachelor's Degree •
Computer Science (Specialization in AI & ML)
Data Analyst Intern
•
Junior
InAmigos Foundation
•
Internship
Completed a short remote internship focused on working with structured datasets and producing assigned analytical deliverables. Collaborated with a distributed team through regular syncs to stay aligned on tasks. Ensured timely delivery of internship outputs based on the project requirements.
Middle Frontend Developer
Confidence: High UI Engineer
UI-focused Frontend Engineer (early-career, middle-level) with a strong eye for polished interactions and visual design. The strongest proven skill is client-side UX engineering and performant animations as shown by the requestAnimationFrame particle system, IntersectionObserver-driven reveal/skill animations and the gallery/modal code in script.js. There is little evidence of framework-based component architecture, automated tests, server-state handling, or accessibility focus management for complex widgets.
UI Component Architecture
4/10
How interface parts are built
Clear, component-like structure in vanilla HTML/CSS/JS with modular functions and CSS tokens but no framework-level component system or design-system packaging.
Evidence
Riddhi-Das-Data-Analyst-Portfolio/script.js: setupGallery, setGalleryIndex, projectData object and modular UI functions
Riddhi-Das-Data-Analyst-Portfolio/index.html: semantic sections (section[id]) and consistent component class names (proj-card, service-card, skill-category)
Riddhi-Das-Data-Analyst-Portfolio/style.css: CSS variables and organized component classes (skill-category, proj-card, modal, etc.)
Responsive & Cross-browser
4/10
Works on all screens and browsers
Responsive breakpoints and mobile menu exist with sensible layout fallbacks; no advanced feature-detection, container queries or explicit RTL/i18n support.
Evidence
Riddhi-Das-Data-Analyst-Portfolio/style.css: media queries for max-width:1024px and 768px adjusting layout and hiding hero image
Riddhi-Das-Data-Analyst-Portfolio/script.js: menuToggle and mobileMenu open/close handling
Riddhi-Das-Data-Analyst-Portfolio/index.html: separate mobile menu markup and accessible form elements
Performance Optimization
5/10
Speed of the interface
Measured front-end performance patterns used: requestAnimationFrame, IntersectionObserver, lazy animations and canvas rendering demonstrate conscious performance work.
Evidence
Riddhi-Das-Data-Analyst-Portfolio/script.js: animateParticles uses requestAnimationFrame and resize handling
Riddhi-Das-Data-Analyst-Portfolio/script.js: IntersectionObserver used for reveal animations, skill bars, ring animations and counters
Riddhi-Das-Data-Analyst-Portfolio/script.js: limited DOM updates and unobserving to avoid repeated work (revObs.unobserve, barObs.unobserve, ringObs.unobserve)
Accessibility & Semantics
3/10
Usable for everyone
Basic accessibility and semantics are present (labels, ARIA attributes, alt text) but keyboard focus management for modals and complete a11y flows are not implemented.
Evidence
Riddhi-Das-Data-Analyst-Portfolio/index.html: form label elements and aria-labels on interactive buttons (back-to-top, modal-close, gallery nav)
Riddhi-Das-Data-Analyst-Portfolio/index.html: alt attributes on images and semantic nav/section structure
State Management & Data Flow
2/10
Managing data in the app
UI state is handled with straightforward variables and functions for the gallery and counters; no server-state discipline, cancellation or complex state machines are present.
Evidence
Riddhi-Das-Data-Analyst-Portfolio/script.js: currentGalleryIndex/currentProjectData state and functions (setupGallery, setGalleryIndex)
Riddhi-Das-Data-Analyst-Portfolio/script.js: handleFormSubmit simple optimistic UI with timeout-based reset
UX & Visual Polish
6/10
Look and feel quality
Strong UX polish with many micro-interactions, preloaders, animated rings, typewriter, tilt and magnetic button effects that improve perceived quality.
Evidence
Riddhi-Das-Data-Analyst-Portfolio/script.js: preloader logic, typeWriter and 3D tilt/magnetic button implementations
Riddhi-Das-Data-Analyst-Portfolio/style.css: polished visuals and components (preloader, ring SVGs, animated skill bars, modals)
Expertise
HTML & CSS• Middle
Web Performance & Optimization• Middle
Industries
Food & Beverages• Middle
Media & Entertainment• Middle
Transportation & Logistics• Middle
Technologies
Frontend
GCP
AWS
Recommendations
- Lead polish-focused UI work: interactive landing pages, marketing sites and portfolios where visual detail and micro-interactions matter.
- Build component libraries and documented design tokens to convert the ad-hoc CSS patterns into reusable components for teams.
- Implement a11y improvements and keyboard/focus management for modals and custom widgets to raise accessibility compliance.
- Add lightweight state management patterns and tests (unit/CI) when integrating with real data or building SPAs to increase reliability.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Intern AI/ML Engineer
Confidence: Low Generalist
An early-career developer at the intern level focused on small AI-related prototypes and simple application scaffolding. The clearest artifact is a README for an AI Studio app that references a GEMINI_API_KEY and Node.js local run instructions, which supports a basic skill in integrating hosted LLM services. There is no demonstrated original model training code, no evaluation or experiment tracking pipelines, and no production MLOps or performance optimization evidence in the provided human-authored files.
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
Verified artifacts
Technologies
AI/ML
Python
SQL
C++
MySQL
PostgreSQL
Snowflake
Copilot
LangGraph
LangChain
Oracle
FAISS
Groq
Jupyter Notebook
SHAP
XGBoost
AI Agents
NLP
Ollama
Pandas
NumPy
Git
Gemini
RAG
Amazon EC2
Anomaly Detection
Recommendations
- Build small hosted-LLM prototypes and UI integrations that use Gemini or similar hosted APIs.
- Develop simple data collection and preprocessing scripts for document or lecture note datasets to gain evidence in data pipelines.
- Implement end-to-end demos that include a minimal evaluation step and a reproducible README so engineering evidence is clear.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories: