I build systems
that hold up
when the interface ends.
Full-stack engineer focused on real-time systems, distributed architecture, and thoughtful interfaces.
Great software is not only
about making features work.
It is about understanding
the constraints around them.
I build across backend architecture, real-time systems, distributed data pipelines, and AI services — always with a focus on reliability, security, and production-readiness over surface-level completion.
CAPSULE
Secure AI-moderated visual vlogging platform with security-first architecture, async processing pipelines, and production observability.
The challenge was not making features work. It was building the security perimeter, async processing, and observability infrastructure that makes a content platform trustworthy at scale.
Architecture Schema
Distributed Node.js backend with AI moderation workers, async queues, and comprehensive observability.
I work across
Select a discipline to see the technologies.
Next.js / React / TypeScript
Node.js / Express / PostgreSQL
Python / FastAPI / Django
Redis / WebSockets / Socket.IO
Docker / CI/CD / Vercel / Railway
Groq AI / LLM integration / RAG
Software Developer Intern — May–Aug 2026
Integration / Debugging /
Performance
Frontend / Product Engineering
Built the CRM Phase 2 frontend from scratch. Designed and implemented the analytics dashboard, Leadership Hub, standardised filter architecture, shared filter context, and reusable component system.
- Dashboard analytics views with date-range filtering
- Leadership Hub — hierarchical performance tracking
- Shared filter context architecture across pages
- Reusable component library and design-system refactor
- Next.js, TypeScript, Tailwind CSS
Integration / Debugging / Performance
Contributed to an existing Node.js/Express trading application (TGGD). Work focused on MW/DW/Wave logic integration, debugging Lightweight Charts performance issues, historical data loading, and data validation — not authorship of the full platform.
- Integrated MW/DW/Wave detection logic into existing trading application
- Resolved Lightweight Charts rendering and performance bottlenecks
- Implemented historical data range handling
- Data validation and audit across trading datasets
- Node.js, Express, WebSocket/chart system
Python Research / Backtesting
Dedicated Python research and backtesting pipeline for MW/DW analysis. Converted CSV equity and NIFTY options datasets to partitioned Parquet, implemented wave detection, state tracking, and analytics pipeline.
- Data quality validation and audit across equity/NIFTY datasets
- Wave detection algorithm implementation
- MW/DW state tracking and event logging
- Excursion analysis and heatmap generation
- CSV → partitioned Parquet conversion pipeline
Data Quality
Validation & audit
Wave Detection
Algorithm implementation
MW/DW State
State tracking & logging
Events / Excursions
Analysis pipeline
Analytics / Heatmaps
Visualization output
Python & Django Backend Developer Intern
MIRA Advanced Engineering
- Developed RESTful API endpoints using Django REST Framework
- Built backend CRUD modules with Django ORM and request validation
- Assisted in backend data processing pipelines and third-party API integration
- Improved backend reliability via structured error responses across endpoints
MW/DW Research
Python backtesting pipeline
Five-phase research infrastructure for market wave detection, state analysis, and backtesting — built independently at TG Levels.
Data Quality
Validation and audit across equity and NIFTY options CSV datasets. Pattern detection for missing, malformed, and outlier entries.
Wave Detection
Algorithm implementation for identifying MW/DW waveform patterns within price series. State-machine driven detection logic.
MW/DW State
State tracking and event logging across market cycles. Building the structured representation of MW and DW phase transitions.
Events / Excursions
Analysis pipeline for excursion events within MW/DW structures. Quantifying and categorising behavioral patterns.
Analytics / Heatmaps
Visualization output and heatmap generation for research results. Converting pipeline output to structured analytical artifacts.
This pipeline was a dedicated research workstream at TG Levels, distinct from the CRM frontend build and the TGGD trading application integration work. CSV equity/NIFTY options datasets were converted to partitioned Parquet for efficient analysis.
The kind of engineer
who reads the whole stack.
I'm a Full Stack Engineer focused on building systems that are reliable, secure, and well-considered — not just functional. My strongest work sits at the intersection of backend architecture, real-time systems, and thoughtful product interfaces.
I gravitate toward problems that involve distributed state, security boundaries, async processing, and high-correctness environments. I care about production readiness: observability, error boundaries, graceful degradation, and meaningful instrumentation — not just features shipping.
Currently interested in backend engineering, platform/infrastructure roles, and full-stack positions where depth matters. Based in India, open to remote opportunities.
Ask about
the work.
An AI that knows the projects, the engineering decisions, the architecture, and the context behind the work. Not a generic chatbot.
Have a difficult
system to build?
I'm available for full-time engineering roles and meaningful freelance projects — especially those involving complex backend systems, real-time infrastructure, or security-critical architecture.
ankitsinghak3028@gmail.com