V. VINOD LOGANATHAN RAMESH KUMAR
Vinod Loganathan Ramesh Kumar

STAFF SOFTWARE ENGINEER · CHARLOTTE, NC

Reliable platforms.
Intelligent operations.

  • Platform Engineering
  • AI Infrastructure
  • Reliability & Security

I build cloud platforms and production AI systems that help engineering teams investigate incidents faster, streamline developer support, and ship with confidence.

Explore my work Preview résumé Download résumé Connect on LinkedIn
ENGINEERING WITH MEASURABLE IMPACT
~40%

Less support triage time for cloud requests

AI-powered Slack support with intelligent routing and knowledge surfacing at Intuit.

Cloud support workflow.
~35%

Lower MTTR for platform incidents

Multi-agent troubleshooting across platform telemetry at Credit Karma.

Platform incident workflow.
1,000+

Developer microservices

Service estate supported by shared cloud workflows at Intuit.

Service estate supported.
Experience and results from my LinkedIn profile ↗

01 / SELECTED WORK

Built for the real world.

Production platforms, applied AI,
and the engineering behind them.

PRODUCTION ENGINEERING · CREDIT KARMA

Making incident response
an agent-assisted workflow.

Platform incidents demand context from many systems. I architected a multi-agent incident debugger that brings telemetry together and coordinates specialized agents to investigate root causes.

Google ADK LangGraph MCP GCP
~35% lower MTTR for platform incidents
Read case study
MULTI-AGENT INCIDENT DEBUGGER
Incident context Signals · symptoms · service
Agent orchestration Domain fan-out · multi-model reasoning
Code context GitHub · Jira · Confluence
Observability Splunk · New Relic · Langfuse
Cloud signals GCP · GKE · Akamai · PagerDuty
Root-cause investigation Correlated evidence · operational context
Conceptual overview of my engineering work
PRODUCTION ENGINEERING · INTUIT

Turning support questions
into guided resolution.

When a developer asks for help in Slack, the assistant routes the request, surfaces the right knowledge, and provides a verified response for the support team.

Slack AgentOps Langfuse GCP
~40% less triage time for cloud support requests
Read case study
AI-POWERED SLACK SUPPORT
01 Ask in Slack Question · service · symptoms
→
02 Route & ground Intent · runbooks · context
→
03 Guide resolution Verified response · handoff
Confluence knowledge GCP service context Langfuse evaluation
Conceptual overview of my engineering work
PLATFORM ENGINEERING · INTUIT

Giving 1,000+ services
a shared operations layer.

AgentOps infrastructure standardizes cloud support workflows across a large developer microservices estate, so teams can trace ownership, gather service context, and move from alert to action with consistent operational tooling.

AgentOps GCP GKE Observability
1,000+ developer microservices supported by shared cloud workflows
Read case study
AGENTOPS PLATFORM
Developer services 1,000+ microservices · owners · dependencies
Shared operations layer Routing · service context · policy · audit
GCP & GKE Telemetry Runbooks Verified actions
Conceptual overview of my engineering work

Open-source projects & engineering labs

All repositories ↗
01 / APPLIED AI

Wikipedia
Research Crew

A local-LLM research workflow that searches Wikipedia, extracts information, and produces a Markdown report. Built with a custom search tool and crewAI orchestration.

Python crewAI Ollama
Explore repository
02 / MLOPS

Model training
to serving

A reproducible ML delivery workflow: train through GitHub Actions, package model artifacts, and build a container image for a Flask prediction service.

GitHub Actions Docker Flask
Explore repository
03 / ML ORCHESTRATION LAB

Kubeflow
Pipelines

Hands-on pipeline examples on Kubernetes, from a first greeting workflow to an Iris classifier using Random Forest. Python pipelines compile to executable YAML.

Kubeflow scikit-learn Kubernetes
Explore repository

02 / EXPERIENCE

A decade of building
what’s next.

From systems and networking to
cloud platforms and AI operations.

JUL 2026 — PRESENT Charlotte, NC

Intuit

Current

Staff Software Engineer

Architecting production AgentOps infrastructure on GCP to automate root-cause analysis and cloud support workflows across 1,000+ developer microservices.

  • Engineered an AI-powered Slack Support Assistant with Langfuse observability, cutting support triage time by approximately 40%.
  • Designed AI-assisted code review pipelines with PII redaction and indirect prompt-injection defenses.
AgentOps Google ADK LangGraph A2A AI security
AUG 2024 — JUL 2026 Charlotte, NC

Credit Karma

Senior Site Reliability Engineer I

Applied multi-agent AI to platform incident response, connecting real-time signals across GitHub, Splunk, New Relic, GCP, and PagerDuty.

  • Reduced platform incident MTTR by approximately 35% with specialized agents, concurrent domain fan-out, and hybrid multi-model strategies.
  • Modernized Kubernetes data-streaming infrastructure by migrating ZooKeeper clusters to KRaft Metadata Quorum.
SRE Multi-agent systems Kubernetes Kafka / KRaft
DEC 2022 — AUG 2024 Oregon

Nike

Senior Software Engineer III ← Senior Software Engineer II

Built and supported a digital asset management platform hosting millions of Nike assets on AWS and Kubernetes.

  • Built CI/CD pipelines and reusable cloud solutions with Crossplane and Terraform.
  • Developed CLI utilities to streamline platform debugging and developer workflows; partnered with engineering teams on platform standards.
AWS Crossplane Terraform Developer experience
JAN 2016 — DEC 2022 Arizona · Oregon

Intel

Senior Cloud Software Engineer

Progressed through software, network, and cloud engineering roles to build multi-cluster orchestration and cloud-native edge capabilities.

  • Developed EMCO, a Kubernetes workload orchestrator for deploying Helm charts across multiple clusters, using Go and MongoDB.
  • Engineered Kubernetes controllers, Go microservices, and Prometheus exporters for AI, analytics, and datacenter telemetry.
Go Kubernetes controllers Edge computing Prometheus

03 / HOW I WORK

Systems thinking.
Hands-on engineering.

My work sits at the intersection of platform engineering, reliability, and applied AI.

I bring a systems and networking foundation to cloud engineering: understand the failure modes, make the platform observable, and build tools that make developers more effective. My recent focus is putting multi-agent systems to work in production operations—with grounding, verification, and security built into the workflow.

Full professional profile
01

Platform & cloud

Designing reliable Kubernetes platforms, cloud infrastructure, and developer tooling.

02

AI systems & operations

Building agentic workflows that connect models, tools, knowledge, and production operations.

03

Engineering & reliability

Improving delivery, observability, incident response, and service resilience.

ENGINEERING PRINCIPLES

Build trust into the platform.

01 / DEVELOPER EXPERIENCE

Platform as a product

Prioritize developer ergonomics and self-service. Make the supported path easy to discover, adopt, and operate.

02 / RESPONSIBLE AUTOMATION

Grounded & defensible AI

Require evidence provenance, sandboxed tools, and human verification for consequential actions. Escalate when the evidence is insufficient.

03 / OPERATIONAL CONTINUITY

Zero-downtime modernization

Aim for continuity through incremental changes, observable rollout gates, and a rollback plan. Evolve systems pragmatically, as with ZooKeeper-to-KRaft modernization.

TECHNICAL SKILLS

AI & agentic engineering

ADK · LangGraph · LangChain · MCP · agentic workflows · autonomous remediation · Vertex AI

Containerization & orchestration

Kubernetes (GKE/EKS) · Docker · Helm · K8s Operators/CRDs · Argo CD · Kustomize

Infrastructure as code

Terraform · Crossplane · CloudFormation · Ansible

Observability & incident response

Splunk · New Relic · Prometheus · Grafana · PagerDuty API and automated workflows

Cloud & edge platforms

GCP (GKE, VPC, Cloud Run) · AWS (EKS, Lambda, S3) · Akamai CDN

Networking & service mesh

Istio · Calico · Flannel · Envoy · service discovery · load balancing

Languages

Golang · Python · Shell scripting · C · C++

Databases & messaging

Kafka (KRaft/Zookeeper) · MongoDB · PostgreSQL · Redis

EDUCATION

University of Southern California

M.S., Electrical & Electronics Engineering · 2014–2015

Ramaiah Institute of Technology

B.E., Electronics & Communications Engineering · 2009–2013

CONTINUOUS LEARNING

Kubernetes certification trilogy

CKA · CKAD · CKS — completed

AI Security Certificate

TryHackMe · Issued August 2026

View credentials on LinkedIn ↗

MANAGER & PEER PERSPECTIVES

Leadership that teammates can speak to.

He's an excellent person, a leader, and he made our team better.
Cody Martin Platform Engineering Manager at Onebrief
Working with him at Nike was fantastic — he joined in and became an effective team member in no time at all.
Grayson Taylor Senior Software Engineer III at Nike
I especially admire his efficiency; he is able to quickly diagnose solutions and solve them at the root, especially when it comes to Kubernetes and Linux.
Anni Shao Software Engineer at Veeva Systems
Read all recommendations on LinkedIn ↗

LET’S CONNECT

Your next platform.
My next challenge.

Let’s talk about Staff and Principal opportunities in platform engineering, AI infrastructure, and reliability.

Connect on LinkedIn Email me Explore my GitHub Based in Charlotte, North Carolina vidonlogan2@gmail.com
× Credit Karma · Incident response

Multi-agent incident debugger

Problem

Platform responders had to reconstruct incidents from code changes, documentation, telemetry, cloud signals, and pager activity spread across separate systems.

My contribution

Designed the investigation flow so specialized agents could gather domain evidence in parallel and return it as a grounded root-cause narrative.

Architecture decision

I chose specialized agents with a final evidence-synthesis step instead of one generalist agent. The alternative was simpler to operate, but it mixed retrieval and reasoning across unrelated systems. Separate agents kept each investigation grounded in its source, while synthesis could cite and compare their evidence.

ADR 01 · Specialized agents with evidence synthesis

Portfolio decision summary

Alternative considered

A single generalist agent offers a simpler operating model, but mixes retrieval and reasoning across unrelated systems.

Trade-offs & consequences

Separate domain agents keep evidence tied to its source. Final synthesis must preserve provenance and reconcile conflicting signals.

Operating constraints

The workflow needs to preserve evidence provenance, avoid premature conclusions, and make the result useful during time-sensitive incident response.

Outcome ~35% lower MTTR for platform incidents.

× Intuit · Developer support

AI-powered Slack support

Problem

Support requests arrived in Slack without consistent routing, service context, or an immediate path to the right runbook.

My contribution

Engineered an assistant that identifies intent, retrieves relevant knowledge and context, and returns verified guidance before a human handoff when needed.

Architecture decision

I used intent-based routing before retrieval rather than searching every source for every question. A single broad retrieval path was easier to build, but it returned noisier context and made evaluation less precise. Routing first narrowed the source set, improving relevance and making unsupported requests safer to escalate.

ADR 02 · Route intent before retrieval

Portfolio decision summary

Alternative considered

Searching every source for each question is easier to build, but returns noisier context and makes evaluation less precise.

Trade-offs & consequences

Routing narrows the source set. Unsupported intents need an explicit escalation path, and each route needs evaluation.

Operating constraints

Guidance must be grounded in current knowledge, traceable for evaluation, and safe to escalate when the assistant cannot resolve the request.

Outcome ~40% less support triage time for cloud support requests.

× Intuit · Platform engineering

Shared AgentOps operations layer

Problem

A large microservices estate needs consistent support workflows while keeping service ownership and operational context clear to individual teams.

My contribution

Architected shared AgentOps infrastructure for routing, service context, policy, telemetry, and auditable operational actions.

Architecture decision

I built a shared control plane for common context and policy instead of a centralized remediation service. Centralizing every action promised tighter consistency, but would have obscured team ownership and created a bottleneck. The control plane standardizes discovery, guardrails, and audit while each service retains its operational decisions.

ADR 03 · Shared context, distributed ownership

Portfolio decision summary

Alternative considered

A centralized remediation service promises consistency, but risks becoming a bottleneck and obscuring service ownership.

Trade-offs & consequences

Share discovery, policy, and audit infrastructure while keeping operational decisions with service teams.

Operating constraints

The platform must scale across independent services, preserve team context, and provide a common operational path without centralizing every decision.

Outcome Shared cloud support workflow scope across 1,000+ developer microservices.

×

Résumé quick view

Vinod Loganathan Ramesh Kumar · Staff Software Engineer

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Résumé at a glance

Platform engineering, AI infrastructure, reliability, and security. Currently Staff Software Engineer at Intuit; previously Credit Karma, Nike, and Intel.

M.S., Electrical & Electronics Engineering, University of Southern California. B.E., Electronics & Communications Engineering, Ramaiah Institute of Technology.

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