You can call me VJ · (he/him)

I’m an engineer with 17+ years of experience spanning application development, infrastructure, platform engineering, security/IAM and production operations, with Linux/Unix as the foundation throughout my career.

My journey began with Linux/Unix production environments, Java/J2EE, enterprise middleware, SOA, JMS/MQ, F5 LTM and 24x7 production support, including application architecture, integration, performance tuning, incident management and cross-layer troubleshooting.

Along the way, I worked with enterprise identity and access management technologies including Oracle Access Manager (OAM), CA SiteMinder, LDAP/Active Directory, SSO and SAML, later extending into cloud IAM, OAuth 2.0 and Kubernetes RBAC.

That foundation evolved through Java/Spring Boot, microservices and Kafka into DevOps, CI/CD, AWS/Azure/GCP, Kubernetes, GitOps, Platform Engineering, SRE and DevSecOps.

I'm now extending that experience into Python, Data Science & ML foundations, AI development and AIOps, exploring how LLMs and agentic systems can help with incident triage, troubleshooting, RCA and operational automation.

📍 Denver, CO
🔎 Open to Senior Platform / SRE / DevOps opportunities

Every generation of technology builds on the previous one. Understanding those underlying layers still matters when debugging modern systems.

flowchart LR
    A["Linux / Unix & Monoliths"] --> B["SOA & Middleware"]
    B --> C["JMS / IBM MQ & F5 LTM"]
    C --> D["Microservices & Kafka"]
    D --> E["DevOps & CI/CD"]
    E --> F["AWS / Azure / GCP"]
    F --> G["Kubernetes & GitOps"]
    G --> H["Platform Engineering & SRE"]
    H --> I["Data Science & ML"]
    I --> J["AI / AIOps & Agentic Systems"]

🤖 What I'm Exploring Now

My current learning and hands-on work connects my platform engineering background with data and AI:

AI agents for DevOps and SRE AI-assisted incident triage and root cause analysis Kubernetes and application log analysis Kafka operational troubleshooting with AI Data Science and Machine Learning foundations RAG and grounded AI systems MCP and agentic workflows Human-in-the-loop operational automation

🚀 Selected Work API v1.4 Migration

Java 17 · Spring Boot 3.x · REST APIs · Oracle

Enterprise API modernisation covering API contract evolution, validation, database integration and performance optimisation.

Cloud & Kubernetes Engineering

AWS · Azure · GCP · Kubernetes · Terraform · Helm · Argo CD

Infrastructure automation, Kubernetes application delivery, GitOps and cloud-native platform engineering.

Messaging Evolution

JMS · IBM MQ → RabbitMQ · Kafka

Experience across traditional enterprise messaging and modern event-driven architectures, including consumer groups, retries, DLQs, message ordering, consumer lag and production troubleshooting.

AI/AIOps Experiments

Python · Splunk AI · Kubernetes · Kafka

Exploring AI-assisted operational troubleshooting, incident triage, log analysis, consumer-lag analysis and RCA using operational data.

📚 Currently Learning

Data Science & Machine Learning foundations Applied LLM engineering Agentic AI systems AI for Platform Engineering and SRE Advanced Kubernetes and cloud-native architecture

📜 Certifications Certified Kubernetes Administrator (CKA) — In Progress Microsoft Azure AI Engineer Associate (AI-102) — In Progress

See the big picture. Break it down. Understand the fundamentals. Stay focused. Build one piece at a time. Then connect the pieces.

I approach technology much like following a recipe:

1. See the big picture — understand the system, its purpose and how the pieces interact.
2. Break it into segments — application, data, infrastructure, networking, security, integration and operations.
3. Understand the fundamentals — programming, Linux, networking, databases, distributed systems and troubleshooting.
4. Add one ingredient at a time — learn, build, test and understand each component before adding the next.
5. Put everything together — understand how the individual pieces behave as one production system.

Technologies change. Strong fundamentals and systematic thinking make learning the next technology much easier.

🥾 Trekking — I enjoy getting outdoors, exploring new places and spending time in nature.

👨‍👧 Family — Outside technology, some of my most valued time is spent with my daughter whenever we get the opportunity to be together.

📚 Continuous Learning — Learning is a regular part of my routine. I learn through university courses, technical conferences, engineering communities and hands-on experimentation.

☸️ Cloud-Native Community — For the past two years, I've regularly followed CNCF livestreams, conference talks and community sessions.

🎓 Technical Learning — I regularly learn from O'Reilly live and recorded events, InfoQ talks and articles, CNCF sessions, university courses and technical conferences, with most conferences attended online.

🔬 Current Interests — Distributed systems, Data Science, Machine Learning, AI engineering, agentic systems, Platform Engineering and SRE.

📫 Connect With Me

💼 LinkedIn: linkedin.com/in/vijaysoundaram 📧 Email: vijay6206@gmail.com