CHITRANGKUMAR JAIN
Building scalable backend infrastructure, distributed systems, and AI-powered applications — engineered for reliability, observability, and outcomes.
Numbers that scale with intent.
A snapshot of the systems shipped, traffic shaped, and tooling adopted.
The four lenses I build through.
How I approach systems, AI, and the boring parts in between.
Platform Engineering
Building scalable internal systems and automation infrastructure that compound team velocity.
Distributed Systems
Designing reliable backend services and microservice communication with strong observability.
Applied AI
Building LLM-powered workflows, retrieval, and intelligent applications that ship to production.
Product Thinking
Engineering solutions aligned with business outcomes — not for the sake of architecture.
Mission history, not a resume.
Each role, the systems owned and the outcomes delivered.
Owning platform reliability and AI integration across an email & engagement infrastructure that processes 10K+ daily transactional emails.
- Designed an AWS SES delivery pipeline with bounce/complaint handling and adaptive throttling.
- Shipped scheduling engines for batched campaigns with retry, dedupe and idempotency guarantees.
- Instrumented end-to-end observability: structured logs, metrics, traces, on-call dashboards.
- Integrated LLM workflows for assisted operations and content generation pipelines.
Three builds, opinionated.
Each project: the problem, the architecture, the trade-offs, the impact.
Finance Aggregator Platform
Secure financial analytics platform.
Finance teams needed a unified view of cross-account balances, transactions and analytics — without compromising on encryption, auditability or auth hardening.
Spring Boot service layer fronting PostgreSQL with JPA, JWT-secured REST APIs, an analytics engine for aggregations, and AES-GCM envelope encryption for sensitive fields. React frontend deployed to AWS.
- Strong encryption without killing query performance
- Multi-tenant data isolation and JWT-based session hardening
- Reliable, idempotent ingestion of transactional data
- Reproducible financial aggregations across time windows
- AES-GCM envelope encryption with per-tenant data keys
- Read-replica friendly analytics queries with materialized rollups
- JWT auth with short-lived access + refresh + revocation list
- Strict input validation, structured audit logs and metric tracing
- Sub-200ms p95 on key analytics endpoints
- Production-grade encryption with zero plaintext at rest
- Auditable change history for every financial mutation
How systems actually talk.
Interactive system diagrams — data flowing through real services.
API gateway → service mesh → encrypted PG, analytics rollups, audit log.
The toolkit, organized.
Not a skill bar in sight. Tools I reach for to ship reliable systems.
Principles I refuse to ship without.
The non-negotiables behind every system I touch.
Reliability First
Build systems that fail gracefully. Retries, timeouts, idempotency and clean rollbacks are not optional.
Observability Matters
Logs, metrics and traces are first-class citizens. If you can’t see it, you can’t operate it.
AI as a Capability
AI should enhance workflows, not replace thinking. Ground every model output in real data and clear UX.
Scale Through Simplicity
Simple systems scale better. Pick the boring, reliable tool first. Add complexity only when measured.
Live control center.
Real-time metrics from this portfolio's backend — pinged from your browser, served by FastAPI.
Open channel. Let's build.
Hiring, collaborating, or comparing notes on distributed systems — I'm listening.
Available for
- Backend Engineering
- Platform Engineering
- Applied AI Engineering
- Distributed Systems