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╚═╝ ╚═╝ ╚═╝╚══════╝╚══════╝╚═╝╚═╝ ╚═══╝╚══════╝ ╚═╝ ╚═╝╚═╝ ╚═╝╚═╝ ╚═╝ ╚═════╝ ╚═════╝ ╚══════╝╚══════╝Building scalable cloud platforms and production-ready AI systems.
Platform engineer focused on designing production-grade cloud systems. I specialize in turning fragmented infrastructure into unified, Git-driven platforms running on Kubernetes, with full lifecycle automation from build to runtime intelligence.
- Platform Engineering: Deployed containerized workloads on EKS using Terraform and GitOps (ArgoCD).
- MLOps & AI Systems: Built end-to-end ML lifecycles and real-time inference pipelines.
- Distributed Systems: Built event-driven pipelines with Kafka and Spark.
- Architecture & Migration: Transitioned monoliths to decoupled microservices, including migrating data storage layers to AWS S3.
- Observability: Integrated comprehensive monitoring and tracing using Prometheus, Grafana, and CloudWatch.
MarketLens AI Platform
An autonomous data pipeline system for continuous market monitoring and behavioral analytics.
- Orchestrated GitOps delivery via Kubernetes and ArgoCD.
- Migrated microservices architecture and data storage to AWS S3.
- Engineered PySpark-based processing pipelines enriched with LLM predictive analytics.
- 🔗 GitHub Org: https://github.com/MarketLens-AI-Platform
Estate Rental Platform — Cloud-Native AI System
A distributed real estate platform utilizing AI-driven scoring.
- Engineered AWS EKS-based microservices managed via Terraform.
- Integrated event-driven communication using Apache Kafka.
- Automated CI/CD pipelines with Jenkins and SonarQube.
- 🔗 GitHub Org: https://github.com/RealEstate-Rental-Project
Real-Time Recommendation Engine
Streaming recommendation system designed for low-latency personalization at scale.
- Built event-driven pipelines with Kafka and Spark.
- Managed collaborative filtering models via MLflow.
- Deployed a FastAPI serving layer with comprehensive production monitoring.
- 🔗 GitHub Org: https://github.com/Nexus-Recommender-AI
- Integrating agentic AI coding tools (Claude Code, Gemini CLI) into infrastructure workflows.
- Designing scalable data architectures for ML-heavy workloads.
- Hardening network-level defense strategies and UNIX system administration.