Sujit Akulwar
Building Scalable
AI-Powered Systems
Full Stack + AI Inspired Software Engineer. I build scalable AI-powered systems from architecture to deployment.
AI Dev OS
Full Stack + AI Engineer
API latency
25%
p95 reduction
Problems
500+
LeetCode solved
Deployments
0
downtime releases
Users
200+
real-time load tested
deploy(architecture, ai, cloud)
latency.p95 --25% | cache.redis enabled
About
A builder fluent across product, backend systems, AI, and cloud.
I enjoy turning complex requirements into reliable software surfaces: polished in the browser and disciplined in production.
Full Stack Developer
Next.js, React, typed APIs, and polished product interfaces that stay fast under real traffic.
Backend Engineering
Node.js microservices, REST APIs, Socket.IO, caching, queues, auth, and database optimization.
AI Systems
LLM APIs, prompt workflows, RAG pipelines, FAISS retrieval, and production AI automation.
Cloud Infrastructure
AWS deployments, Lambda, DynamoDB, Docker, Nginx, and CI/CD pipelines built for repeatability.
DevOps
GitHub Actions, GitLab CI/CD, containerized services, zero downtime release patterns.
Performance Optimization
Profiling, MongoDB aggregation tuning, Redis caching, p95 latency work, and frontend migrations.
System Timeline
Design
Design product flows, API contracts, data paths, and interaction states.
Build
Ship typed frontend surfaces, backend services, integrations, and AI workflows.
Optimize
Profile latency, tune queries, cache hot paths, and harden deployment loops.
Scale
Package with Docker, automate CI/CD, and deploy cloud-native services.
Experience
Research, AI, and full-stack delivery in production contexts.
A timeline of engineering work across enterprise systems, AI services, cloud deployment, and performance-focused backend improvements.
Tata Consultancy Services
TCS Research Division
Full Stack Developer · Assistant System Engineer
StandardWings Technologies
Full-Stack AI Engineer Intern
Projects
Interactive systems with real-time, cloud, and AI foundations.
Project cards open into deeper previews with stack badges, feature surfaces, and links for source or demos.
Skills
A production-minded stack for AI-powered applications.
From typed interfaces to retrieval pipelines, the skill map is optimized around systems that are fast, observable, and pleasant to use.
Languages
Frontend
Backend
Databases
Cloud/DevOps
AI
AI / Research
A control-center view for applied AI systems.
The AI layer focuses on grounded retrieval, automation APIs, measurable behavior, and deployment-ready service boundaries.
Neural Runtime
RAG + LLM orchestration
LLM Core
AI Runtime
Retrieval
Agents
Memory
Streaming
Vector DB
Inference
LLM integrations
Production pipeline primitive
RAG pipelines
Production pipeline primitive
AI APIs
Production pipeline primitive
Intelligent systems
Production pipeline primitive
ML automation
Production pipeline primitive
Production AI systems
Production pipeline primitive
Achievements
Measured outcomes across algorithms, performance, releases, and leadership.
The portfolio emphasizes proof: solved problems, faster APIs, stable deployments, and systems that hold up under real usage.
0+
LeetCode problems solved
Algorithmic consistency across core DS&A patterns.
0%
p95 latency reduction
Redis caching and backend optimization in production services.
0
downtime deployment target
Dockerized release flow and CI/CD automation.
0+
concurrent users simulated
Real-time trading and WebSocket load behavior.
Contact
Let’s build the next intelligent system.
Reach out for full-stack, backend, AI integration, platform, and cloud engineering opportunities.