Ram Polarapu
Kubernetes Engineer
Bash
Go
PowerShell
Python
Java
Infrastructure as Code: 6/10
Containerization & Orchestration: 4/10
Active 9 hours ago
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Overview
Technical skills
Timeline
Roles
Overview
Middle-level cloud/DevOps engineer focused on AWS EKS and Terraform provisioning with practical multi-language container workflows. The strongest proven skill is infrastructure provisioning and remote state discipline demonstrated by the Terraform bootstrap (S3 + DynamoDB) and environment modules in chatops-ai-platform-infra/bootstrap/backend-state/main.tf and environments/dev/main.tf. Observability, advanced CI patterns, rollout/rollback strategies and production-grade Kubernetes hardening are not evidenced in the public code.
Technical skills
Bash
Go
PowerShell
Python• Middle
Java• Junior
Python
Boto3
FastAPI
Uvicorn
SQLAlchemy
Flask
Java
Spring Boot
Databases
PostgreSQL
AI/ML
Copilot
DevOps
Amazon EC2
AWS
AWS Lambda
Azure
Azure AKS
Azure DevOps
Blue-Green Deployment
CI/CD
Configuration Management
Git
GitLab CI
Platform Engineering
Service Mesh
SLI/SLO/SLA
Docker Compose
Containers
Rest API
Amazon EKS• 6y+
CloudFormation• 6y+
Grafana• 6y+
Kubernetes• 6y+
Prometheus• 6y+
Terraform• 6y+
Jenkins• 5y+
ArgoCD
GitHub Actions
GitOps
Helm
OpenTelemetry
Cryptography
Vault
Timeline
Kubernetes & Platform Engineer
•
Middle
EPAM Systems
•
Full-Time
Architected and maintained enterprise Kubernetes platforms on Amazon EKS for 50+ engineering teams using a multi-cluster design and GitOps workflows. Built standardized microservice and AI inference deployment patterns with Helm and CI/CD pipelines using Jenkins and GitHub Actions, including security scanning. Developed reusable Terraform modules for AWS infrastructure provisioning and improved provisioning speed significantly. Enhanced observability with Prometheus, Grafana, and OpenTelemetry and supported GPU-enabled inference environments with Kubernetes scheduling controls.
Amazon EKS
Kubernetes
Terraform
ArgoCD
Helm
GitHub Actions
Jenkins
Prometheus
Grafana
OpenTelemetry
GitOps
Indiana Wesleyan University
Master's Degree •
Data Analytics
Platform Engineer (Infrastructure Automation)
•
Middle
LTIMindtree
•
Full-Time
Led migration of 30+ legacy on-prem applications to AWS EKS by designing cluster architecture, implementing Prometheus/Grafana monitoring, and automating deployments. Built an Infrastructure-as-Code framework with Terraform and AWS CloudFormation templates for repeatable multi-environment provisioning. Designed CI/CD pipelines with Jenkins and GitLab CI for 30+ microservices and improved release efficiency and deployment reliability. Implemented high-availability EKS setups with multi-AZ deployments and autoscaling and improved incident response using centralized monitoring and logging.
Amazon EKS
Kubernetes
Terraform
CloudFormation
Jenkinssince 2021
Prometheus
Grafana
Cloud Infrastructure & Automation Engineer
•
Middle
Cognizant
•
Full-Time
Provisioned and managed scalable AWS infrastructure on EKS using Terraform, including Kubernetes cluster setup, autoscaling, and network segmentation. Automated environment provisioning with AWS CloudFormation and Terraform templates to standardize development, staging, and production. Implemented monitoring and alerting using Prometheus and Grafana with AWS CloudWatch to improve operational visibility and reduce downtime. Supported CI/CD pipeline automation with development teams and applied Kubernetes security best practices such as RBAC, network policies, and secrets management.
Amazon EKSsince 2020
Kubernetessince 2020
Terraformsince 2020
CloudFormationsince 2020
Prometheussince 2020
Grafanasince 2020
Middle DevOps Engineer
Confidence: Medium Cloud Architect
Middle-level cloud/DevOps engineer focused on AWS EKS and Terraform provisioning with practical multi-language container workflows. The strongest proven skill is infrastructure provisioning and remote state discipline demonstrated by the Terraform bootstrap (S3 + DynamoDB) and environment modules in chatops-ai-platform-infra/bootstrap/backend-state/main.tf and environments/dev/main.tf. Observability, advanced CI patterns, rollout/rollback strategies and production-grade Kubernetes hardening are not evidenced in the public code.
CI/CD Pipelines
3/10
Automated build and deploy
Basic CI pipeline that builds, pushes images and applies manifests; lacks reusable workflows, caching, matrix builds, deploy gates or rollback steps.
Infrastructure as Code
6/10
Managing servers with code
Repeated Terraform usage with proper remote-state bootstrap (S3 + DynamoDB locks), environment separation and well-structured modules; relies on community modules rather than many custom modules or infra tests.
Evidence
RamPo312/chatops-ai-platform-infra/bootstrap/backend-state/main.tf: S3 bucket + DynamoDB table for Terraform remote state and locking
RamPo312/chatops-ai-platform-infra/environments/dev/main.tf: terraform modules for VPC and EKS with variables and node group configuration
RamPo312/eks-devops-project1/terraform/main.tf: use of terraform-aws-modules/vpc/aws and terraform-aws-modules/eks/aws
Containerization & Orchestration
4/10
Working with containers
Concrete multi-language containerization and basic Kubernetes manifests; includes a multi-stage Java build and docker-compose for local dev but lacks tuned probes, resource requests/limits, PDBs or advanced rollout constructs.
Evidence
RamPo312/ecommerce-platform-app/order-service/Dockerfile: multi-stage Maven build then runtime image
RamPo312/ecommerce-platform-app/docker-compose.yml: multi-service local compose bringing up frontend, product-service, order-service and Postgres instances
RamPo312/eks-devops-project1/k8s/deployment.yaml: Kubernetes Deployment manifest used for cluster deploy
Observability & Monitoring
1/10
Watching system health
Minimal observability evidence beyond basic health endpoints; no dashboards, alerting rules, SLOs or structured tracing present.
Reliability & Incident Response
1/10
Keeping systems up
Limited reliability and incident response artifacts; simple replica counts and local compose restarts exist but no runbooks, PDBs, graceful shutdown tuning or explicit rollout/rollback strategy in manifests.
Cloud & Cost Optimization
3/10
Smart use of the cloud
Cloud provisioning and cluster configuration present (EKS, VPC); basic cost awareness is visible through single NAT gateway and managed node group sizing but no autoscaling policies, spot/eviction strategies or rightsizing analysis.
Expertise
Cloud Platforms & Architecture• Middle
Infrastructure as Code• Middle
Industries
Commerce• Middle
Technologies
Containers
Go
PowerShell
Bash
Terraform• 6y+
Flask
Helm
Azure DevOps
GitHub Actions
OpenTelemetry
CloudFormation• 6y+
Prometheus• 6y+
GitLab CI
Azure
CI/CD
GitOps
ArgoCD
Jenkins• 5y+
Git
AWS
Kubernetes• 6y+
Grafana• 6y+
Uvicorn
Boto3
Blue-Green Deployment
Platform Engineering
Service Mesh
Configuration Management
Amazon EKS• 6y+
Azure AKS
AWS Lambda
Amazon EC2
SLI/SLO/SLA
Cloud• mentioned only
DevOps• mentioned only
Docker• mentioned only
Recommendations
- Implement reusable CI workflows with caching, concurrency controls and deploy gates; add explicit rollback or progressive delivery steps to the pipeline.
- Add production-grade Kubernetes hardening: liveness/readiness probes, resource requests/limits, PodDisruptionBudget, anti-affinity and explicit termination/graceful-shutdown handling.
- Expand observability: add Prometheus metrics scraping, Alertmanager rules or SLO-based alerts, and at least one dashboard expressed as code to prove monitoring and alerting practices.
- Develop infra tests and drift detection workflows (terratest or conftest policies) and create a documented remote-state migration/runbook for environment changes.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Junior Data Scientist
Confidence: Medium Generalist
Backend microservice developer (junior) focused on small Python services and SQL-backed APIs. The strongest proven skill is implementing a simple FastAPI service with SQLAlchemy models and DB seeding, as shown in product-service/app/main.py and product-service/app/models.py. There is little to no evidence of testing, CI/CD automation, scalability design, or data-science work in the public artifacts.
Statistical Rigor
Correct use of statistics
Not evidenced in public code
Data Wrangling & Cleaning
Preparing and cleaning data
Not evidenced in public code
Exploratory Analysis & Visualization
Exploring and visualizing data
Not evidenced in public code
Predictive Modeling
Building models that predict
Not evidenced in public code
Business Insight & Impact
1/10
Turning analysis into business value
Minimal business framing is present via basic REST endpoints that return product lists, but there is no evidence of linking outputs to business metrics or error-cost reasoning.
Reproducibility & Notebook Hygiene
2/10
Clean, repeatable analysis
Basic reproducibility measures exist such as a pinned requirements.txt and an environment-driven DATABASE_URL; however there is no evidence of CI pipelines, data versioning, or tests.
Industries
Commerce• Middle
Technologies
PostgreSQL
Recommendations
- Implement additional features for small backend services: input validation, error handling, pagination and CRUD endpoints using the existing FastAPI + SQLAlchemy pattern.
- Add automated tests and a CI pipeline to validate API behavior and DB migrations; include simple integration tests that exercise the seeded database.
- Dockerize the service and create reproducible deployment manifests or a simple GitHub Actions workflow to build and push images.
- Improve observability and production-readiness by adding structured logging, metrics, and graceful startup/shutdown handling.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories:
Junior Backend Developer
Confidence: Medium API Engineer
Backend API engineer (Middle) focused on building small polyglot microservices and simple REST endpoints. The strongest proven skill is implementing basic service endpoints and database-backed APIs as shown by ecommerce-platform-app/product-service/app/main.py and ecommerce-platform-app/order-service/src/main/java/com/ecommerce/orderservice/OrderController.java. There is little evidence of production-grade concerns such as migrations, auth and authorization, retries and backoff, advanced observability, or paging and rate limiting.
API Design
2/10
How well APIs are designed
Basic REST endpoints exist but there is minimal API design discipline - no versioning strategy, no pagination or filtering, and no idempotency or error contract consistency visible in human-authored backend code.
Evidence
ecommerce-platform-app/product-service/app/main.py: GET /products implementation returns entire result set without pagination or filters
ecommerce-platform-app/order-service/src/main/java/com/ecommerce/orderservice/OrderController.java: GET /orders returns repository.findAll() with no pagination or API versioning
Data Layer & Database
3/10
Working with databases
Data layer shows direct use of ORM and basic session handling and JPA entities but lacks migration history, explicit transaction boundaries beyond simple commit, and no evidence of isolation-level awareness or hand-tuned queries.
Evidence
ecommerce-platform-app/product-service/app/main.py: Base.metadata.create_all(bind=engine) and SessionLocal usage in seed_products with commit/finally db.close()
ecommerce-platform-app/order-service/src/main/java/com/ecommerce/orderservice/model/OrderEntity.java: JPA entity plus OrderRepository usage in OrderController
Scalability & Performance
1/10
Handling load and speed
Almost no scalability or performance engineering is present - no caching, no queueing, no rate limiting, and endpoints that return full datasets can be problematic at scale.
Evidence
ecommerce-platform-app/product-service/app/main.py: get_products returns all products from products = db.query(Product).all() with no pagination or caching
ecommerce-platform-app/order-service/src/main/java/com/ecommerce/orderservice/OrderController.java: getOrders uses orderRepository.findAll() which may return large datasets without streaming or pagination
System Architecture
3/10
Overall system structure
There is a simple microservice decomposition across languages with clear service boundaries but it reads as starter-level microservices without documented inter-service contracts, resilience boundaries, or explicit config/secret management in the human-authored code.
Evidence
ecommerce-platform-app/frontend/src/index.js: Express-based frontend service exposing basic endpoints
ecommerce-platform-app/product-service/app/main.py: standalone FastAPI product service with its own DB setup
ecommerce-platform-app/order-service/src/main/java/com/ecommerce/orderservice/OrderController.java: standalone Spring Boot controller for order service
Security & Auth
1/10
Protecting data and access
Little to no security controls are visible in the human-authored backend code - no auth/authz, no input validation beyond basic ORM usage, and no secrets handling patterns in the analyzed files.
Evidence
ecommerce-platform-app/product-service/app/main.py: public GET /products endpoint with no authentication or authorization
ecommerce-platform-app/frontend/src/index.js: simple Express server without auth or input validation
Reliability & Observability
2/10
Stability and monitoring
Minimal reliability and observability features are present; basic health endpoints exist but there are no structured logs, metrics, retries, timeouts, graceful shutdowns, or retry/backoff strategies in the human-authored code.
Evidence
ecommerce-platform-app/product-service/app/main.py: /health endpoint implemented but no metrics or logging instrumentation
ecommerce-platform-app/order-service/src/main/java/com/ecommerce/orderservice/OrderController.java: /health endpoint and simple controller but no observability hooks
Expertise
Python• Junior
Java• Junior
Microservices & API Architecture• Junior
Databases & Vector Storage• Junior
Industries
Commerce• Middle
Technologies
Python• Middle
Java• Junior
Rest API
Docker Compose
Spring Boot
SQLAlchemy
FastAPI
ArgoCD• mentioned only
CI/CD• mentioned only
CI/CD• mentioned only
DevOps• mentioned only
Docker• mentioned only
GitOps• mentioned only
Kubernetes• mentioned only
Stack• mentioned only
Recommendations
- Build small-to-medium REST APIs that need straightforward CRUD and DB integration, including writing unit tests and simple integration tests.
- Develop product or order service features for e-commerce MVPs where quick iteration and multi-language glue are acceptable.
- Work on backend tasks that extend current projects with migrations, input validation, auth flows, and basic observability (metrics/logging) to harden services for production.
Repositories
The developer's experience in this domain has been verified based on AI analysis of the following repositories: