Backend, Cloud & DevOps
Fractional CTO | Software Architect | SaaS | AI Agents
Fractional CTO | Software Architect | SaaS | AI Agents
10x throughput · Kafka · 13 social channels · built for viral-scale message bursts
Redesigned an enterprise social platform's data layer into an event-driven architecture on Apache Kafka, ingesting and analysing real-time social data from 13 channels.
Sized for viral-scale bursts of 250–1,000 messages/minute per customer, with hybrid delivery paths: streaming (~15–20s average latency) and polling (~2 min), with a worst-case 5-minute latency ceiling.
Also set architecture standards across application, data, and AI, and rebuilt the React/TypeScript frontend for multilingual workspaces with automated WCAG 2.2 / ADA Section 508 accessibility checks. End-to-end throughput improved 10x. Confidential client engagement.
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Cold start: 17 min → under 3 min. Shard loading: 16 min → 50 sec. Serving cost: ~1/6 of a managed SageMaker equivalent.
Performance-engineered a LLaVA vision-language deployment on AWS as part of a specialised strike team, with the full write-up published on LinkedIn (linked under Completed Work).
Reduced end-to-end cold start from ~17 minutes to under 3 (66%) via custom Deep Learning AMIs (−32% immediately), ephemeral-storage model caching with parallel aria2c downloads (checkpoint loading 16 min → 50 sec), and 4-bit NF4 quantization (shard loading 41s → 18s). Along the way, diagnosed a storage-path bug where checkpoint shards kept re-downloading despite being baked into the AMI.
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Cut shipping time 40% with a production multi-agent system built around governance, not demos. Confidential client engagement.
Designed and deployed a multi-agent system covering the full product lifecycle: PRD generation → system design → multi-repo execution → code review → compliance/security checks → release management. Shipping velocity improved 40% across cross-functional teams.
Built on a Backstage-based engineering knowledge base with MCP connectors to JIRA, GitHub, Slack, and Teams; LLM workloads on OpenAI and Anthropic via A2A-style agent orchestration, implemented in Python/Bash tooling.
Shipped with an enterprise AI governance layer: hallucination detection with knowledge-base remediation, model drift monitoring, and audit logging for regulated environments.
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90M registered users · 17 countries · 99.99% uptime · $451M disbursed via DBT
Led Everwell Hub from a monolith to a horizontally scalable microservices platform (Java/Python, Docker/Kubernetes, GCP, CI/CD, IaC). As chief architect I personally designed the service decomposition; as CTO I led ~50 engineers plus system-integrator partners and the WHO/Ministry of Health partnership.
Deployed across 17 countries and scaled India's national TB platform (Ni-kshay) to ~90M registered patients and staff, with 6 lakh+ direct users, at 99.99% uptime, sub-second P95 latency, and 2h RTO / 4h RPO disaster recovery. The payments module disbursed $451M through direct benefit transfers.
Released the platform as MIT open source with full wiki, runbooks, and API contracts — adopted by partner health systems since.
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Tech Team Builder & Software Architect
Tech Team Builder & Software Architect
Tech
Full Stack Rapid Prototyping – Vision to Product in Weeks 🚀
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Full Stack Rapid Prototyping – Vision to Product in Weeks 🚀
Full-stack dev | Clean modern UI | AI Powered | Deployment
Full-stack dev | Clean modern UI | AI Powered | Deployment
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Senior Backend Developer
Senior Backend Developer
Full-Stack Dev | React, Node & FastAPI | Fixing bugs fast.
Full-Stack Dev | React, Node & FastAPI | Fixing bugs fast.
Enterprise News Aggregator: Clean Architecture & State Orchestration:
Architected a production-ready news aggregation platform using Flutter and Dart, centered on strict Clean Architecture principles and the MVVM pattern. This project serves as a blueprint for scalable, decoupled application design, featuring an event-driven state management system (BLoC).
Technical Deep-Dive:
State Orchestration: Leveraged the BLoC pattern to handle complex UI states, ensuring a strictly reactive and predictable data flow.
Scalable Architecture: Implemented a decoupled Data Layer with the Repository Pattern, allowing for seamless swapping of data sources (e.g., GNews, NewsData) without affecting business logic.
Advanced Networking: Engineered a robust networking layer using Dio, featuring custom interceptors for error logging and automated retry logic.
Optimized UX: Developed a "Smart Search" functionality with debouncing to minimize API overhead and improve client-side performance.
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Economic Intelligence Dashboard: Predictive Modeling & Real-Time Visualization:
Architected a high-fidelity full-stack platform designed to aggregate and visualize macroeconomic indicators from the World Bank Open Data API. This project combines live data orchestration with machine learning to provide predictive insights into global GDP, inflation, and labor trends.
Technical Deep-Dive:
Data Pipeline: Engineered a Python/Flask backend to normalize and serve real-time macroeconomic data, ensuring consistent data structures across diverse international sources.
Predictive Analytics: Implemented a regression-based forecasting tool that utilizes historical time-series data to project future GDP growth with interactive user-input variables.
Geospatial & Statistical UI: Developed a responsive frontend using React and Leaflet.js, featuring dynamic map hotspots and complex statistical overlays using Plotly.
Insight-Driven Design: Optimized the platform for data storytelling, allowing users to reveal hidden correlations between variables like inflation and unemployment through interactive scatter plots.
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