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JiginJayaprakash/README.md
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β•‘          JIGIN JAYAPRAKASH                               β•‘
β•‘          Technical Lead Β· Cloud Architect Β· AI Systems   β•‘
β•šβ•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•β•

LinkedIn Email Location

Designing distributed systems that scale β€” from architecture blueprints to production.


$ whoami

I'm a Technical Lead with 10+ years engineering cloud-native, AI-integrated platforms for global enterprises. I operate at the intersection of system architecture, engineering leadership, and AI/ML integration β€” turning complex business problems into reliable, observable, and scalable systems.

Currently leading a cross-geography team of 8 engineers at NielsenIQ, owning technical roadmap, architectural guardrails, and delivery across multiple release cadences.


$ cat architecture.yml

principles:
  - Microservices & Domain-Driven Design
  - Event-Driven Architecture
  - API-First Β· Observability-by-Default
  - Zero-Downtime Deployments

cloud:
  primary:  Azure  # Functions Β· App Services Β· Logic Apps Β· Cosmos DB
  learning: AWS

backend:
  languages: [C#, .NET 8, Python, Go]   # Go β†’ actively building
  frameworks: [ASP.NET MVC, Django]

frontend:
  stack: [React.js, Angular, TypeScript]
  styling: [Tailwind CSS, Bootstrap]

data:
  search:   [Elasticsearch, Kibana]
  sql:      [MS SQL Server, PostgreSQL]
  nosql:    [Azure Cosmos DB, MongoDB]
  streaming: Kafka   # learning

ai_ml:
  platforms:  [Azure AI Foundry, Azure AI Vision, Google Vision API]
  tooling:    [LLM Integration, OCR Pipelines, Ollama, GitHub Copilot]

devops:
  ci_cd:       [Azure Pipelines, Octopus Deploy]
  observability: [Structured Logging, Kusto, Performance Dashboards]

$ grep -r "impact" ./career --count

Metric Result
πŸ”» Change Failure Rate ↓ 5% via architectural guardrails
⚑ API Latency Improvement ↓ 80% through strategic data indexing
πŸ“¦ On-Time Delivery Rate 95% across release cycles
πŸ‘₯ Team Scaled 3 β†’ 8 engineers across 4 countries
πŸš€ Deployment Cadence 2Γ— / month zero-downtime releases
πŸ€– AI Pipeline Built OCR + LLM replacing full manual rule-based system
πŸ’° Azure Cost Reduction Audit-driven log stream cleanup

$ ls ./projects

πŸ“¦ test-dashboard/
β”‚   β”œβ”€β”€ Python Django API
β”‚   β”œβ”€β”€ React.js frontend
β”‚   β”œβ”€β”€ Elasticsearch + PostgreSQL
β”‚   └── Dockerized Β· Observability-first architecture
β”‚
πŸ“¦ ecommerce-platform/
β”‚   β”œβ”€β”€ MERN Stack (MongoDB Β· Express Β· React Β· Node)
β”‚   β”œβ”€β”€ Auth Β· Payments Β· Admin workflows
β”‚   └── Tailwind CSS Β· B2C marketplace patterns

$ tail -f ./learning.log

[ACTIVE]  Go / Golang         β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‘β–‘  building production projects
[ACTIVE]  Apache Kafka         β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‘β–‘β–‘β–‘  event streaming patterns
[ACTIVE]  AWS Core Services    β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‘β–‘β–‘β–‘β–‘  cloud-agnostic architecture
[ACTIVE]  LLM Integration      β–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–ˆβ–‘β–‘  Ollama + local model pipelines

Not just tutorials β€” shipping real projects to reach production-level exposure.


$ ping jigin

Open to conversations on distributed systems, AI-integrated architectures, and engineering leadership at scale.

LinkedIn Email


⚑ Currently @ NielsenIQ · Mumbai, India

Pinned Loading

  1. developer-analytics-dashboard developer-analytics-dashboard Public

    A dashboard with random data generated with api, built with github codespace

    Python 2

  2. ECommerce ECommerce Public

    Ecommerce demo

    TypeScript 3

  3. resolve-textplus-srt-importer resolve-textplus-srt-importer Public

    Automate subtitle import in DaVinci Resolve by converting .srt files into Fusion Text+ titles directly on the timeline β€” with randomized font styling and proper timing.

    Lua