name: Gopi Chinnappa
role: Tech Manager & Enterprise Architect
based_in: Bengaluru, Karnataka, India 🇮🇳
focus: [ platform engineering, cloud security, MLOps/AIOps, GitOps,
AI, LLMs, data engineering ]
mission: ship secure, boring-to-operate infrastructure that scales
languages: [ English, Kannada, Hindi, Telugu, Tamil ]- 🏗️ 15+ years designing and running enterprise-scale cloud and container platforms across AWS, Azure, GCP and Nutanix — landing zones, multi-account governance, cost control.
- ☸️ Deep in Kubernetes — scheduling, multi-tenancy, day-2 ops, and everything GitOps: Argo CD, Flux, Helm, Kustomize, Cluster API, Karpenter, Istio, Cilium, KEDA.
- 🔁 CI/CD & IaC end to end: Jenkins, GitHub Actions, Azure DevOps, GitLab CI, Tekton with Terraform, Terragrunt, Ansible, Packer and Crossplane.
- 📈 Observability & SRE: Prometheus, Grafana, Loki, Tempo, OpenTelemetry, Thanos, ELK — SLOs, error budgets, actionable alerting over dashboards nobody reads.
- 🤖 Building the pipes that carry Data/AI/ML workloads to production — MLflow, Kubeflow, Airflow, Feast, Ray, Triton, KServe: feature stores, model CI/CD, drift detection, GPU scheduling.
- 🧠 AI / LLM engineering: LangChain, LlamaIndex, MCP, Ollama, vLLM, Hugging Face, plus vector stores (pgvector, Qdrant, Pinecone, Weaviate) — RAG pipelines, evals, guardrails, agentic workflows.
- 🧊 Modern data stack: Snowflake, Databricks, Azure Data Factory, Spark, Delta Lake, dbt, Kafka, Superset — lakehouse design, ELT orchestration, lineage and governance.
- 🔐 InfoSec by default: policy-as-code (OPA/Gatekeeper, Kyverno), supply-chain integrity (Sigstore/Cosign, SBOM, Trivy), secrets hygiene (Vault, External Secrets), zero-trust networking and CIS/SOC 2 hardening.
- 🏥 Interested in healthcare data interoperability — FHIR, HL7v2, X12, OMOP, dbt analytics data models and PHI-safe pipelines.
- 🤝 Open to collaborating on Kubernetes tooling, Golang, GitOps and LLM-Ops projects.
🐍 Watch my contributions get eaten
I'm always up for a conversation about enterprise architecture, Kubernetes at scale, lakehouse design, or getting AI/LLM systems safely into production.
⭐ From gopihc — thanks for stopping by!

