Platform / SRE / DevOps / Cloud / AI and Agentic Systems Alpharetta, GA · linkedin.com/in/amit-patole · amit.patole@gmail.com

My career started with a screwdriver, not a keyboard. In the late 1990s I was assembling and repairing PCs, everything from 486s to the first Pentium IVs, then reimaging desktops and running helpdesks for enterprise clients in Pune. It taught me the habit I have never lost: understand the machine from the metal up, and never call something fixed until you have watched it work.

From there I grew into servers and networks, and then into running a technical support department where I built Linux hosting platforms, automated site and database provisioning with scripting, and first learned that the real leverage in operations is removing the human from the repetitive path. At BMC Software I stepped up to enterprise platform administration, keeping high-availability and disaster-recovery environments healthy for the application suites businesses ran on.

Telecom is where I became a platform engineer. At Tech Mahindra I led the platform for a hosted carrier VoIP service on an OpenStack private cloud, working hands-on with the network functions that carry real calls for millions of subscribers. At VIAVI Solutions I became the primary cloud and platform architect for telecom systems deployed with operators across 23+ production markets: Kubernetes and GitOps across multi-cloud and air-gapped environments, release automation that turned slow, error-prone deployments into something teams can rely on, and a security framework that audits every change. When production breaks, I am the escalation lead.

Working deep in AI platforms crystallized a question I had been circling for years: when a model produces an output, what does it actually mean for the work to be done? Not that a pipeline succeeded or a test passed, but done in the sense that you would stake your name on it. A 2026 paper I published explored one edge of that question, asking whether quantum optimization can route work across a multi-agent LLM cascade in ways classical greedy methods miss.

• Design and operate production Kubernetes and GitOps platforms (ArgoCD, Terraform, Helm) across multi-cloud and air-gapped environments.
• Ship measurable outcomes: meaningful cloud savings, faster release cycles, and safe, repeatable releases across 200+ microservices.
• Build agentic-AI systems: self-hosted LLM inference, multi-agent workflows, RAG, tool-use enforcement, and hallucination validation.
• Work forward-deployed with Tier-1 operators, translating customer workflows into shipped platform features.

The open-source work here is the engineering answer to that same question. The Verel organism and its standalone companions are built on one conviction that has followed me the whole way, from the screwdriver to the model: nothing is done, and nothing compounds, until a grader returns a verdict. It is a fleet of pip-installable "organs" that grade an agent's outputs on one shared contract.

• tickerpulse-ai - 24x7 stock news monitoring across 10+ free sources.
• quantum-llm-routing - QAOA-based quantum optimization for LLM cascade routing. First QUBO formulation of the model-selection problem, benchmarked on IBM Quantum hardware.
• claude-genkit-plugin - Firebase Genkit plugin for Claude Code.
• gapcert - Publicly-trusted TLS certificates for air-gapped and egress-restricted fleets.

Platform and SRE: Kubernetes (AKS, k3s), ArgoCD, Terraform, Helm, Ansible, Jenkins, Docker, Podman Cloud: AWS, Azure, GCP, multi-cloud networking, on-prem and air-gapped Data: Kafka, ClickHouse, Apache Iceberg, PostgreSQL, Snowflake, TimescaleDB AI and Agentic: Anthropic Claude API, Claude Code, LangChain, LangGraph, vLLM, Ollama, RAG Languages: Python, SQL, Java, Bash

_{Open to Forward-Deployed Engineering, Platform, and AI-infrastructure roles.}