I engineer reliable enterprise infrastructure, automate complex operations, modernize platforms, and turn practical ideas into working solutions.
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Migrations, automation, storage, DR, cyber resilience and modern infrastructure.
02BhoomiSetu, Deal Guru, Advaitha and practical cloud experimentation.
03How my experience evolved from storage and SAN to hybrid and AI/ML infrastructure.
0430-second, 3-minute and detailed technical views, plus the conventional resume.
Delivered outcomes alongside forward-looking AI + infrastructure initiatives currently being explored.
Direct migration scope across production, test and sandbox, including VDI and application workloads.
Cisco SAN upgrade automation across a two-location environment; broader upgrade cycle improved from roughly six months to about one month.
Storage/SAN operations, replication, DR and data protection across enterprise platforms.
Recovered production and test file shares after a ransomware incident using an isolated recovery network and Pure Storage as the clean migration target.
GPU server build and platform readiness for ornithology AI/ML workloads, including PCIe, BIOS/iDRAC, thermal and infrastructure considerations.
GPU utilization, storage capacity and compute-health dashboards for proactive infrastructure monitoring.
Operational pattern for querying infrastructure logs, correlating alerts/incidents and routing actionable events into ITSM workflows.
Workload assessment, VLAN/logical-network mapping, Azure Migrate replication, phased cutover, validation and rollback planning.
Proactive Infrastructure Intelligence
Exploring telemetry-driven analysis for anomaly, capacity and performance-risk insight across hybrid infrastructure.
Continuous Log, Incident & RCA Intelligence
Exploring continuous log/event correlation, incident-draft assistance and RCA acceleration using enterprise LLMs with engineer review.
Click a story to see the challenge, engineering approach and outcome.
Environment: enterprise-scale workloads enterprise estate across production, test and sandbox.
My scope: VMware-to-Nutanix workloads, including VDI and application workloads, including systems supporting Oracle databases.
Platform: Nutanix clusters built on Dell server infrastructure.
Environment: 46 enterprise storage arrays and 495 Cisco SAN switches distributed across two locations, supporting production, test and sandbox environments.
The challenge: SAN switch software upgrades were performed manually. Engineers had to stage the appropriate Cisco software images on each switch, validate available device storage and compatibility, and execute the upgrade workflow. At fleet scale, a complete upgrade cycle could take approximately six months.
Automation approach: We developed Ansible playbooks for Cisco SAN switch upgrades and initially validated the workflow against test switches before expanding the rollout. Upgrade jobs were launched through the enterprise Ansible controller (AWX / Ansible Tower, depending on the platform in use), with the playbooks maintained as controller projects/job templates.
Operational improvement: A switch upgrade could typically complete in roughly 20 minutes, depending on chassis and module count. With controlled automation and parallelized rollout planning, the broader switch upgrade cycle was reduced from roughly six months to about one month.
Challenges encountered: Automation was not simply “write a playbook and run it.” We encountered controller job failures and suspended jobs, controller resource/capacity constraints, insufficient switch filesystem/bootflash space for software images, upgrade tasks terminating mid-workflow, and timeout issues during long-running device operations.
Engineering lessons: The solution evolved to emphasize pre-checks before change execution: image and platform validation, available-space checks, controller capacity, timeout tuning, controlled batches, failure handling, post-upgrade validation and safe recovery procedures.
Why this matters: This case study demonstrates automation at infrastructure scale, including the operational failures and safeguards required to turn a manual network-maintenance process into a repeatable production workflow.
Challenge: Improve recovery repeatability and reduce DR failover effort.
Engineering: SRM protection groups, automated recovery plans, RTO/RPO validation and storage replication architecture.
Incident: A ransomware/security incident affected approximately 260+ NAS/file shares across production and test environments in a legacy Dell EqualLogic-backed file-share environment.
Isolation: Established a segregated recovery network before restoration so the recovery target remained separated from the affected environment.
Recovery approach: Provisioned the required storage and access on Pure Storage, established controlled connectivity, migrated the affected shares and performed recovery/access validation before restoring business access.
Outcome: Recovered and migrated the affected shares to Pure Storage and decommissioned the affected legacy environment after successful recovery validation.
Research use case: The Lab works with bird-observation data captured through distributed camera systems. The resulting data can support research into bird activity, behavior and movement patterns and can also contribute to public-facing educational content.
My infrastructure scope: Build and configure Dell PowerEdge R7725 GPU server infrastructure according to the compute, memory, GPU, storage, networking and availability requirements of AI/ML research workloads. My role is infrastructure enablement—not development of the bird-analysis AI/ML models.
Compute platform: The PowerEdge R7725 is a 2U dual-socket AMD EPYC 9005 platform supporting up to 192 CPU cores per socket. Depending on the validated configuration, it can support accelerator options including NVIDIA L4, L40S, H100 NVL, H200 NVL and other supported GPUs.
Not every engineering win is a migration. Some of the most valuable work is connecting platforms so operations become safer, faster and more observable.
Splunk/CMDB expiry visibility → ServiceNow ticket → PowerShell CSR → Microsoft CA → Azure Key Vault → approval → Ansible deployment/activation across Windows, Linux, vCenter, storage, Cisco and load-balancer/API infrastructure.
Enterprise backup/recovery, replication and recovery validation combined with isolated recovery networks, clean-target migration and controlled restoration for ransomware/cyber-recovery scenarios.
Splunk queries and infrastructure events → investigation/correlation → ServiceNow incident/change/problem workflow → RCA and governed remediation.
Prometheus/Grafana infrastructure dashboards plus Splunk operational investigation for compute, GPU, storage and platform health.
Replication between enterprise arrays, VMware SRM protection/recovery planning, failover validation and RTO/RPO-focused operations.
Ansible/AWX-AAP and PowerShell applied beyond one device type: SAN upgrades, health checks, certificates and repeatable infrastructure operations.
Personal projects. Real problems. Working solutions. A place to show curiosity, product thinking and practical cloud engineering.
One platform for land discovery, marketplace intelligence, agriculture guidance and land-related information with an English + తెలుగు experience.
Goal: Help shoppers identify the real best deal by comparing product quantities and normalized unit pricing—not just the advertised price.
Goal: Make school and daily reminders simple through Telegram instead of requiring another dedicated mobile app.
A concise recruiter view. The final site can expand each role into selected deliveries without reproducing the entire résumé.
Dell PowerEdge R7725 AI/GPU infrastructure, Kubernetes GPU nodes, QNAP storage and backup, Active Directory OU migration, Prometheus/Grafana monitoring and platform operations.
Large-scale Nutanix migrations, VMware, enterprise storage, SAN, automation, cloud storage integration and observability.
Hybrid infrastructure integration, storage migrations, NetApp, Dell EMC, Nutanix and enterprise project delivery.
Enterprise storage and SAN/NAS operations, legacy storage migrations, self-help tools and leadership of 20+ engineers.
End-of-life storage modernization, EMC-to-VMAX migrations, SRDF and host-based cutovers, performance analysis and storage provisioning.
Enterprise NAS/SAN support, Celerra-to-Isilon migrations, Data Domain, storage upgrades and mentoring across global operations teams.
A recruiter can understand the profile quickly; a hiring manager can open engineering stories; a technical interviewer can explore architecture and design decisions.
Professional identity, core capabilities and measurable impact.
Career journey, selected engineering deliveries and Innovation Lab.
Architecture, technical decisions, challenges, troubleshooting and lessons learned.
The conventional document remains available as a supporting artifact—not the entire experience.
Prototype note: public launch should remove phone details from the page, verify all quantified claims, confirm employer-confidentiality boundaries, and connect final Resume / LinkedIn / project URLs.
Infrastructure • Automation • Hybrid Cloud • Modernization
Why the infrastructure exists: Bird-observation data captured by distributed camera systems can be brought back to the Lab for processing and research analysis. Researchers can use the resulting datasets to study activity, behavior and movement patterns, while selected outputs can support educational/public content.
My responsibility: Translate AI/ML workload requirements into infrastructure: server configuration, CPU/GPU sizing, memory, local/high-speed storage, network connectivity, firmware/platform readiness, GPU enablement, operating environment and health monitoring.
Compute capability: Dell PowerEdge R7725 is a 2U system with two 5th-generation AMD EPYC 9005 processors and up to 192 cores per processor—up to 384 CPU cores at the platform maximum. Dell documents support for GPU options including NVIDIA L4, L40S, H100 NVL and H200 NVL, subject to the exact riser, thermal, power and chassis configuration.
GPU density: Dell documents up to six single-width L4 GPUs, while the RC4 riser supports up to two dual-width GPUs. Exact accelerator choice should follow the AI/ML workload profile rather than simply selecting the largest GPU.
Engineering considerations: GPU memory and compute needs, CPU-to-GPU balance, system RAM, PCIe layout, NVMe/storage throughput, network bandwidth, power/cooling, supported firmware/driver stack, monitoring and future growth.
Important scope distinction: This portfolio story demonstrates infrastructure engineering for AI/ML workloads. It does not claim ownership of the research models or scientific analysis performed by the research teams.
Experience across enterprise storage and SAN platforms, replication between arrays, DR setup, VMware SRM, recovery planning and RTO/RPO validation. Resume examples include PowerStore Active-Active replication and SRM recovery workflows.
Incident: A ransomware/security incident affected approximately 260+ NAS/file shares across production and test environments in a legacy Dell EqualLogic-backed file-share environment.
Recovery objective: Separate recovery activity from the affected environment and restore the file-share services onto a clean storage target with controlled access.
Isolation: Established a segregated recovery network to keep the recovery target separated while restoration and validation were performed.
Target platform: Provisioned the required storage and access on Pure Storage and established the connectivity needed for the recovered shares.
Migration & validation: Migrated the affected file shares to the Pure Storage environment and performed recovery/access validation before controlled restoration of business access.
Outcome: Approximately 260+ shares were recovered and migrated to Pure Storage, followed by successful decommissioning of the affected legacy environment after recovery validation.
Engineering takeaway: Cyber resilience is more than having a backup. Isolation, a clean recovery target, controlled access, validation, deliberate reintegration and retirement of affected infrastructure are all part of the recovery design.
Pure Storage • Dell EqualLogic-backed file services • NAS / SMB-CIFS • Network Segmentation • Ransomware Recovery • Cyber Resilience • Data Migration • Recovery Validation
Infrastructure monitoring for GPU utilization, storage capacity and compute health. The AI/ML platform work includes Prometheus/Grafana monitoring and GPU visibility, with the goal of surfacing infrastructure conditions before they become workload-impacting issues.
Use Splunk to query and investigate infrastructure logs/events, correlate technical evidence with incidents, and connect actionable operational events to ServiceNow incident/change/problem workflows. This also provides the foundation for the future LLM-assisted incident and RCA concept.
Assess VMware CPU, memory, storage, network utilization and application dependencies; design Azure Local target clusters; map VMware VLANs/port groups to logical networks; use Azure Migrate discovery/replication; execute phased cutover and validate networking, applications, monitoring and backup with rollback readiness.
Goal: Use approved infrastructure telemetry and operational signals to identify abnormal behavior, capacity pressure and emerging performance risk before they become service-impacting incidents.
Potential sources: Nutanix, VMware, enterprise storage/SAN, Prometheus/Grafana and other approved monitoring telemetry.
Concept: Telemetry / metrics → preprocessing and context → Vertex AI + Gemini analysis → anomaly/risk summary → recommended investigation → engineer review.
Guardrail: This is an R&D direction, not a claimed production implementation. AI provides analysis and recommendations; production remediation remains controlled by engineers and governed automation.
Goal: Continuously analyze approved infrastructure logs/events, correlate related symptoms and reduce the time engineers spend assembling incident context.
Potential sources: Splunk logs, ServiceNow incidents, infrastructure alerts/events and historical operational knowledge.
Concept: Logs + alerts + ITSM context → enterprise LLM served through an appropriate private/enterprise AI platform → correlation and summarization → incident-draft assistance → probable RCA / troubleshooting suggestions → engineer review.
NVIDIA role: NVIDIA NIM can be considered as an enterprise model-serving option; the exact LLM should be selected and validated for the use case rather than claiming “NVIDIA LLM” as one specific model.
Guardrail: This remains an exploration/POC direction. Incident creation, RCA acceptance and remediation stay governed by human review and existing operational controls.
Context: The organization operated an estate of approximately enterprise-scale workloads across production, test and sandbox. My direct migration scope was VMware-to-Nutanix workloads.
Workloads: VDI and application VMs, including systems supporting Oracle database workloads.
Target platform: Nutanix clusters built on Dell server infrastructure.
Engineering approach: workload assessment, readiness and dependency validation, migration waves, cutover planning, rollback readiness and post-migration validation.
Business rationale: The modernization was also driven by the need to reduce VMware licensing/infrastructure cost and simplify platform operations. The public portfolio explains the cost-reduction strategy without publishing an exact savings figure until that number is separately approved for external disclosure.
Portfolio principle: the overall environment scale and my direct delivery scope are intentionally shown separately.
Environment: 495 Cisco SAN switches and 46 enterprise storage arrays across two locations, spanning production, test and sandbox.
Before: software images were staged and upgrades executed manually; a fleet cycle could take about six months.
Automation: Ansible playbooks executed through the enterprise automation controller, first validated against test switches and then expanded through controlled rollout.
Result: an individual switch upgrade could typically run in roughly 20 minutes depending on chassis/module count, while the broader upgrade cycle was reduced to about one month.
Challenges: failed/suspended controller jobs, controller resource pressure, insufficient switch image-storage space, upgrade termination and timeout handling.
Lessons: pre-check image/platform compatibility, available space, controller capacity and timeouts; use controlled batches, failure handling, recovery procedures and post-upgrade validation.
01 — Vertex AI + Gemini: Proactive Infrastructure Intelligence
Explore telemetry from Nutanix, VMware, storage, SAN and observability platforms to surface anomalies, capacity pressure and performance risk earlier.
02 — Nutanix Enterprise AI + Enterprise LLMs: Incident & RCA Intelligence
Explore Splunk logs, ServiceNow incidents and infrastructure events for correlation, summarization and RCA assistance.
Both are intentionally labeled exploration/R&D. AI provides insight and recommendations; production remediation remains controlled by engineers and governed automation.
Platform setup: Nutanix clusters were built on Dell server infrastructure. The work required treating compute, storage, networking, cluster health and operational readiness as one platform rather than independent components.
Migration preparation: The source VMware estate contained production, test and sandbox workloads. My direct migration scope was VMware-to-Nutanix workloads, including VDI and application VMs and systems supporting Oracle database workloads.
Implementation approach: assess workloads and dependencies → validate target capacity/readiness → group migration waves → perform controlled cutover → validate application/network/storage behavior → monitor after transition.
Challenges & failures: Not every workload should be treated the same. Dependencies, migration windows, application behavior, database sensitivity, network reachability, storage performance and rollback requirements can change the migration approach. Failures and exceptions need to be isolated rather than allowing one workload to disrupt an entire wave.
Successful transition: A migration is complete only after workload health, connectivity, application access, performance and operational ownership are validated on the Nutanix platform.
Before upgrade: review cluster health, capacity/headroom, alerts, hardware/firmware dependencies, software compatibility, backup/recovery readiness and the approved maintenance window.
Plan the sequence: understand the lifecycle components and dependencies, define the supported upgrade path, confirm workload resiliency, communicate impact and avoid unnecessary concurrent infrastructure changes.
During execution: monitor cluster health and workload availability continuously. Treat warnings, node/service issues or failed checks as decision points rather than automatically pushing forward.
Failure handling: capture the failed stage, preserve logs/evidence, validate cluster stability, determine whether to retry, pause or escalate, and avoid turning a recoverable component issue into a broader outage.
After upgrade: validate cluster health, nodes/CVMs, storage, networking, VM availability, performance and monitoring/alerting before declaring the maintenance successful.
Portfolio note: This describes my lifecycle engineering approach. We will add exact Nutanix upgrade incidents and versions only after validating the specific real-world examples.
Before: inventory, ownership, dependencies, capacity, compatibility, migration wave design, change approval and rollback planning.
During: controlled batches, clear checkpoints, exception handling, communication and real-time validation.
After: VM health alone is not enough. Validate network reachability, storage, application/service functionality, database dependencies, performance, monitoring and support handoff.
Lesson: Successful modernization is a controlled transition of services and operations—not simply movement from one hypervisor to another.
I started my infrastructure career close to the foundation: enterprise storage, SAN, provisioning, availability and operational support. That foundation expanded into virtualization and VMware, then HCI and Nutanix modernization, automation, hybrid cloud and now GPU/AI infrastructure enablement.
Today: I bring these layers together—compute, virtualization, storage, SAN, DR, automation, monitoring, ITSM and modern AI/ML infrastructure—rather than treating them as isolated technologies.
Go deeper into architecture, challenges, failures, lessons and outcomes.
Purpose: Bring fragmented land, location, marketplace and agriculture information into a simpler cloud-based experience for Andhra Pradesh users, including NRIs.
Goal: Let a user begin with a location and progressively explore land information, available listings and practical agriculture/land guidance through an English + తెలుగు experience.
Working Prototype • Active Development
BhoomiSetu is a personal Innovation Lab project and is not presented as a replacement for official government land records or professional legal due diligence.
Senior Cloud Operations & Infrastructure Engineer with 13+ years across virtualization, HCI, enterprise storage/SAN, DR, automation, hybrid infrastructure and AI/ML infrastructure enablement.
Recruiter contact actions now include email, mobile, LinkedIn and the latest resume.