Nithin Kumar Meena · Full Stack · ML · Backend · Systems
✓ AI inference pipelines → 40,000+ images processed daily, <80ms latency
✓ Real-time systems → multi-user collaboration, sub-100ms update propagation
✓ Production backends → payment controllers, compliance modules, S3 pipelines
✓ NLP pipelines → resume parsing at 94% extraction accuracy, <1.2s parse time
✓ Scalable APIs → REST + microservices, shared Node.js / Go schemas
4+ years of production software. Former CTO of a healthcare tech startup - designed the architecture, hired the team, shipped the product. Now building distributed systems and targeting remote-first engineering roles.
Production AI system · 40K+ images/day · deployed
Batched inference architecture with async preprocessing, GPU memory pooling (fp16), and Redis job queuing. Built to handle variable load with horizontal scaling and automatic retry on failure.
PyTorch Python Redis Docker Node.js AWS S3
Ingestion → Redis Queue → GPU Workers (batch=32, fp16) → MongoDB + S3 → Webhook
Production · 94% extraction accuracy · <1.2s parse time
Extracts structured data from unstructured resumes across PDF and DOCX formats, multiple layouts, noisy text. Used in a live candidate screening workflow.
Python spaCy Transformers FastAPI Docker
Multi-user · live presence · conflict resolution
Sub-100ms update propagation with Socket.io. CRDT-inspired sync model handles concurrent edits across 50+ simultaneous users without conflicts.
React Node.js Socket.io MongoDB Redis
| Project | What it is | Status |
|---|---|---|
| Distributed Task Queue | Bull-based job queue with priority, retry, dead-letter | 🔨 In progress |
| URL Shortener | Go + Redis with analytics, rate limiting, custom slugs | 🔨 In progress |
| Go REST API Boilerplate | Production-grade: auth, logging, graceful shutdown, tests | 🔨 In progress |
| AI Agent Framework | LLM-driven task agents with tool use and memory | 🔜 Starting soon |
| Kubernetes Lab | Self-hosted cluster, Helm charts, observability stack | 🔜 Starting soon |
Languages
Backend
Frontend
Data
AI / ML
Infrastructure
Currently working through a self-directed curriculum to level up to Staff Engineer over the next 2 years:
- CS Foundations — OSTEP (Operating Systems), Computer Networks, Distributed Systems (in progress)
- Go mastery — language internals, concurrency patterns, building production services
- Cloud & Infrastructure — AWS solutions architecture, Kubernetes, observability
- Distributed Systems — consensus algorithms, event sourcing, CQRS patterns
- System Design — designing at scale, case studies, architecture reviews
Open to remote roles at product companies solving hard engineering problems.
