AI Engineer

team lexguard  • Peerlist

• Designed and deployed an end-to-end ML-based warehouse optimization system to reduce resource wastage and improve logistics efficiency • Engineered feature pipelines using Python and Pandas, processing structured operational data to train predictive models • Applied optimization algorithms to improve inventory allocation, reducing simulated lead time and storage cost • Evaluated model performance using cross-validation, achieving robust generalization across unseen data distributions

Warehouse Optimization System ML Project · Py

Built a distributed incident root-cause analysis platform for DevOps/SRE teams ingesting live Prometheus alerts. → Feedback-aware RAG pipeline (LlamaIndex + ChromaDB) auto-generates root-cause hypotheses — cutting mean triage time by ~70% → Non-blocking FastAPI endpoints sustaining sub-200ms P99 latency under high-throughput alert bursts → Multi-tenancy, JWT auth, rate-limiting; deployed on Render + Vercel with CI/CD via GitHub Actions → Built chaos simulator to stress-test incident detection across microservices Stack: Python · FastAPI · LlamaIndex · ChromaDB · PostgreSQL · Prometheus · Docker · Next.js Live Url - https://sentinel-sre-zeta.vercel.app

Sentinel-SRE — AI-Powered Incident RCA Platfo

Multi-tenant SaaS automating DPDP Act compliance audits for enterprises using RAG + LLM pipelines. → LangChain + Groq + Google GenAI pipeline delivers clause-level risk flagging with remediation steps → Next.js 14 dashboard with live audit widget, Supabase auth, and role-based access control → FastAPI backend containerised on AWS EC2 with Docker; frontend optimised to <1.5s LCP → Real-time legal document analysis with automated clause extraction and risk scoring at scale Stack: Next.js · FastAPI · LangChain · Supabase · AWS EC2 · Docker · PostgreSQL Live Url - https://lexguard-ai-three.vercel.app