01
Application Engineering
Java systems built for the real world.
Spring Boot microservices, secure REST and GraphQL APIs, Kafka ingestion, data consistency, resilience patterns, and test automation.
Dallas, Texas · Open to senior engineering conversations
Senior Software Engineer Cloud Platform Engineer
Engineering the invisible systems that make ambitious software dependable.
I design cloud-native applications and platform foundations that turn secure delivery, resilient operations, and infrastructure standards into repeatable engineering capabilities.
9+Years engineering
3Major cloud platforms
EnterpriseScale & governance
DEPLOYMENTGitOps synced
POSTURERestricted
AI BOTAaruRelease intelligence assistant
RUNTIME99.99%target availability
01 · Engineering profile
Core expertise
Application engineering, platform work, observability, and agentic AI in one portfolio section.
01 · Engineering profile
Core expertise
02
Platform Engineering
Golden paths with guardrails.
Reusable Terraform, Helm promotion controls, Kubernetes policy, GitOps workflows, and cloud foundations that reduce drift.
03
SRE & Observability
Signals that shorten decisions.
RED/USE metrics, SLO dashboards, Kafka lag, traceable logs, automated runbooks, and incident-learning loops.
04
AI-Assisted Engineering
Release intelligence, not AI theater.
Azure OpenAI, Microsoft Foundry, MCP tools, knowledge-enhanced agents, risk summaries, API deprecation analysis, and RCA preparation.
20+engineer-hours saved per release cycle
02 · Measured impact
Key results
Quick evidence of scale, reliability, and delivery improvements.
02 · Measured impact
Key results
Environments governed
Helm compatibility and promotion guardrails supporting safer delivery across EKS and OpenShift estates.
Pods operated
Enterprise workloads across highly available Kubernetes, Rancher, and EKS clusters.
Faster pipelines
Parallel execution and dependency caching improved CI/CD throughput without weakening controls.
Improved MTTR
Observability, correlation IDs, and AI-assisted runbook triage accelerated incident resolution.
03 · Selected work
Case studies
Three readable examples instead of a long landing-page narrative.
03 · Selected work
Case studies
01
Enterprise release governance
Promotion guardrails across 200+ environments.
Designed Helm compatibility and release controls for Kubernetes and OpenShift, including pre-release channels, upgrade checks, API deprecation awareness, and safer promotion practices.
35%pipeline acceleration
200+environments covered
GitOpsdesired-state delivery
02
Production reliability
Resilience for a 1,500+ pod platform estate.
Implemented autoscaling, disruption budgets, production-readiness checks, resource optimization, observability, and incident triage patterns for enterprise container platforms.
18%compute cost reduction
27%MTTR improvement
40%fewer release failures
03
AI-assisted release engineering
Engineering copilots grounded in release evidence.
Applied knowledge-enhanced agents to release notes, risk summaries, API deprecation deltas, Helm diffs, and incident-response preparation—improving readiness without removing human control.
20+hours saved per cycle
Traceablesource-grounded outputs
Humanapproval retained
04 · Experience
Career timeline
Roles, companies, and what changed at each step.
04 · Experience
Career timeline
2024 — Present
Turnberry Solutions Inc. · Dallas, TX
Senior Software Engineer / Cloud Platform Engineer
- Spring Boot, secure APIs, Kafka ingestion, and cloud-native systems
- Helm and Terraform guardrails across 200+ environments
- AI-assisted release intelligence and engineering agents
2019 — 2024
CGI Inc. · Lafayette, LA
Software Engineer / Lead DevOps Engineer / SRE
- Java EE modernization into Spring Boot microservices
- 1,500+ pod Kubernetes, Rancher, and EKS operations
- CI/CD, observability, canary delivery, and incident engineering
2019
Magtech · Vienna, VA
Cloud Engineer
- Jenkins delivery automation and SonarQube quality gates
- Vault Enterprise on GCP with least-privilege controls
2018 — 2019
Veridic Solutions · New York, NY
Cloud / DevOps Engineer
- Cross-regional cloud routing and Terraform infrastructure
- GKE observability and reliability controls
2014 — 2016
Intelnet · Hyderabad, India
Linux Administrator / IT Analyst
- Led a 10-member data-quality and performance team
- Built Java automation that reduced manual review effort by 40%
05 · Toolkit
Technology stack
A compact breakdown of application, platform, cloud, delivery, reliability, and AI tools.
05 · Toolkit
Technology stack
Application
Java 7–21, Spring Boot, Spring Cloud, Hibernate/JPA, REST, GraphQL, gRPC, Resilience4j, Feign
Platform
Kubernetes, EKS, OpenShift, Rancher, Docker, Helm, Terraform, Argo CD, Vault, Ansible
Cloud & Data
AWS, GCP, Azure, Kafka, RabbitMQ, PostgreSQL, Oracle, MongoDB, DynamoDB, Redis
Delivery
Jenkins, GitHub Actions, GitLab CI, Maven, Gradle, SonarQube, SAST/DAST, Testcontainers
Reliability
Prometheus, Grafana, Splunk, Dynatrace, CloudWatch, Kibana, SLO/SLI dashboards, RCA automation
Agentic AI
Azure OpenAI, Microsoft Foundry, Microsoft 365 Agents SDK, MCP tools, grounded agents, model evaluation
06 · Operating philosophy
How I build
Security, observability, and automation with judgment.
06 · Operating philosophy
How I build
06 · Operating philosophy
Secure by default.
Observable by design.
Automated with judgment.
The most valuable platform is not the one with the most tools. It is the one that makes the right path easy, the risky path visible, and every production change explainable.
securityContext:
runAsNonRoot: true
readOnlyRootFilesystem: true
allowPrivilegeEscalation: false
capabilities:
drop: ["ALL"]
seccompProfile:
type: RuntimeDefaultPod Security: restrictedNetwork: default denySupply chain: scanned
07 · Credentials
Education and certifications
Academic foundation plus verified engineering and AI learning.
07 · Credentials
Education and certifications
Credentials
Computer science foundation. Continuous engineering growth.
Master of Science in Computer Science
Troy University · Troy, Alabama
HackerRank Software Engineer
Problem Solving · Basic & Intermediate
Microsoft Learn Agentic AI
Azure OpenAI · Foundry agents · MCP · model evaluation
08 · Testimonials
Professional recommendations
First-hand perspectives from people who have worked alongside me.
08 · Testimonials
Professional recommendations
Recommendations in progress
Good engineering is a team effort.
I’m collecting thoughtful recommendations from engineering leaders, teammates, and delivery partners. Only genuine, first-hand feedback will appear here.
First-hand onlyShared with permissionRole and context included
Start a conversation
Building a platform, modernizing a system, or raising the engineering bar?
Let’s talk about the architecture, delivery model, and operational foundations needed to make it last.