- 🚀 4+ years building and scaling ML/MLOps systems on Kubernetes for enterprise customers
- ☸️ CKA & CKAD certified — designing and operating production-grade Kubernetes infrastructure across EKS, GKE, and AKS
- 🏢 Onboarded 15+ B2B enterprise customers onto SaaS ML platforms; reduced onboarding time from 2 hours to 15 minutes
- 📊 Built multi-cluster monitoring dashboards tracking 50+ enterprise Kubernetes clusters with real-time SLA compliance
- 🤖 Deployed LLMs and predictive ML models on GPU-accelerated Kubernetes clusters (KServe/MLServe), achieving 1.5x throughput increase
- 🔬 Published researcher — NeurIPS 2019, thesis on NLP terminology extraction, and a filed US patent on adaptive autoscaling
- 📫 Reach me at jayanthkul@gmail.com
Senior Kubernetes Engineer (2021 – Present)
- Architected automated ML inference deployment pipelines using Kubernetes, ArgoCD, and Redis — cut deployment time from 2 days to 12 hours and eliminated 90% of manual errors
- Built agentic AI systems (LLM-based) for pod capacity estimation and conversational microservice analytics with 1-year historical data retention
- Led enterprise customer workshops (AWS, Kubernetes), resulting in 5+ enterprise POCs
- Implemented cross-cloud E2E test automation (Golang/Ginkgo) across EKS, GKE, AKS, catching 85% of issues before production
ML Research Engineer — NLP (2018 – 2021)
- Developed hybrid terminology extraction algorithm achieving 96% top-100 hit rate; improved document embedding F1 by 9.41%
- Built a scalable knowledge graph for 4M+ research papers using PyTorch (TransE/TransH) with Elasticsearch-backed semantic search
Deep RL Engineer — IISc & Siemens Research (2017 – 2018)
- Published multi-agent deep RL solution for traffic optimization at NeurIPS 2019; reduced traffic density by 33%
- ☸️ Certified Kubernetes Administrator (CKA)
- ☸️ Certified Kubernetes Application Developer (CKAD)
