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Lauris Neimanis — Senior DevOps & Platform Engineer

AWS Certified Solutions Architect – Associate · Terraform/Terragrunt · AWS · ECS · Kubernetes · GitOps · Ansible · Linux · Observability

I design, modernize, automate, and operate business-critical infrastructure across AWS, hybrid cloud, and on-premises environments.

My strongest value is long-term infrastructure ownership: taking systems from manual or inconsistent operations toward clear architecture, repeatable delivery, reliable observability, controlled change, and maintainable platform standards.

I have 16+ years of hands-on experience across production infrastructure, B2B consulting, public-sector systems, and product-company environments.


Target Roles

The strongest fit is a senior role where infrastructure reliability, platform ownership, and pragmatic modernization matter:

  • Senior DevOps Engineer
  • Senior Platform Engineer
  • Infrastructure Architect
  • Cloud Infrastructure Engineer
  • SRE-oriented Infrastructure / Platform role

Certification


Production Engineering Experience

My production engineering experience includes:

  • AWS and hybrid infrastructure ownership across long-lived environments
  • Terraform modules and Terragrunt environment composition
  • ECS Fargate and ECS-on-EC2 application platforms
  • Reusable web, worker, scheduled-task, and multi-container delivery patterns
  • CI/CD, controlled releases, rollback, and environment-isolated review workflows
  • Ansible, Bash, Python, and Linux automation
  • Metrics, logs, dashboards, alerting, and operational feedback loops
  • Managed databases, queues, cache, shared storage, object storage, and supporting platform services
  • Regulated-environment infrastructure controls: segmentation, logging, hardening, recovery, and audit readiness
  • Production troubleshooting, root cause analysis, incident response, backup, restore, and disaster recovery
  • Staged modernization from standardized Docker/ECS delivery toward Kubernetes where it is operationally justified

My container-platform experience spans both ECS and Kubernetes, including production Kubernetes and Helm operations together with hands-on EKS and AKS platform engineering.


What I Bring

  • Production ownership: I understand uptime, recovery, incidents, audits, and operational consequences, not only build pipelines.
  • Platform thinking: I separate infrastructure, platform services, delivery workflows, and workloads into clear ownership boundaries.
  • Pragmatic modernization: I prefer staged, reversible improvements over large rewrites that create operational risk.
  • Infrastructure as Code discipline: I care about state, locking, module boundaries, dependency drift, CI validation, and long-term maintainability.
  • Reliability focus: I treat observability, backups, access control, and failure modes as design inputs, not afterthoughts.
  • Strong Linux and networking base: I am comfortable below the abstraction layer when debugging production systems.

Public Reference Work

The repositories below are public reference implementations. They are intentionally scoped to demonstrate architecture, ownership boundaries, delivery patterns, and operational reasoning without exposing non-public production systems.

Project What it demonstrates Technologies
AWS EKS Platform AWS infrastructure foundation for EKS Terraform, EKS, VPC, IAM, ACM
AWS EKS GitOps Platform and workload delivery above the cluster boundary Argo CD, ApplicationSets, Helm, Kustomize
Karpenter Platform Platform-owned capacity, scheduling, cost, and disruption model Karpenter, Spot/On-Demand, ARM64, GitOps
Autoscaling Platform Governed workload scaling as platform policy HPA, KEDA, Kyverno, Argo CD
Observability Stack GitOps-managed metrics, logs, dashboards, and alerting Prometheus, Alertmanager, Grafana, Loki, Alloy
Azure AKS Platform Azure infrastructure foundation using the same platform principles Terraform, AKS, VNet, Managed Identity
Infrastructure Automation End-to-end AWS provisioning and server bootstrap reference stack Terraform, Ansible, cloud-init, Docker
Application Demo Small app used to validate delivery and infrastructure automation FastAPI, Streamlit, Docker, GHCR

Supporting repositories:

Repository Purpose Technologies
AWS Terraform Backend Bootstrap Shared S3 remote-state backend bootstrap with native locking Terraform, AWS S3
Azure Terraform Backend Bootstrap Azure Blob remote-state backend bootstrap with native locking Terraform, Azure Storage

Platform Architecture Model

Terraform infrastructure
        ↓
Kubernetes cluster foundation
        ↓
Capacity and scheduling layer
        ↓
Autoscaling and policy layer
        ↓
GitOps reconciliation
        ↓
Platform services
        ↓
Application workloads

The model is intentionally layered:

  • Infrastructure is provisioned with Terraform and cloud-native APIs.
  • Capacity and scheduling are owned by the platform, not individual applications.
  • Autoscaling behavior is governed as policy through approved profiles.
  • GitOps reconciliation provides controlled, reviewable desired-state changes.
  • Platform services are managed independently from application workloads.
  • Workloads express runtime intent without owning infrastructure details.

This is the same operating principle I use in production work: define clear contracts between layers so systems remain understandable, auditable, and maintainable as they grow.


Selected Project Details

AWS EKS Platform

Terraform · AWS · EKS · IAM · Networking · ACM · Cloudflare

A production-aligned AWS EKS infrastructure foundation focused on deterministic networking, controlled cluster access, and clear infrastructure/platform/workload boundaries.

Highlights:

  • Explicit VPC, subnet, routing, NAT, and security topology
  • EKS Managed Node Groups using Amazon Linux 2023
  • IAM-native Kubernetes access through the EKS Access API
  • EKS Pod Identity add-on for workload identity
  • ACM certificate automation with Cloudflare DNS validation
  • Shared S3 Terraform backend with native locking
  • Infrastructure-only scope; GitOps and workloads are managed separately

Repository: https://github.com/LaurisNeimanis/aws-eks-platform

AWS EKS GitOps

Argo CD · GitOps · Kubernetes · Helm · Kustomize · ApplicationSets

A production-aligned GitOps delivery layer for platform services and workloads above an existing EKS infrastructure boundary.

Highlights:

  • Out-of-band Argo CD control plane
  • App-of-Apps bootstrap pattern
  • ApplicationSets for repeatable onboarding
  • Helm and Kustomize-based delivery
  • Clear separation between platform services and workloads
  • Git as the source of truth for desired Kubernetes state

Repository: https://github.com/LaurisNeimanis/aws-eks-gitops

Karpenter Platform

Karpenter · Kubernetes · GitOps · AWS · Platform Engineering

A platform-owned capacity and scheduling layer for EKS. The platform owns node architecture, cost, capacity type, consolidation, and disruption; workloads express resource requirements without controlling infrastructure details.

Highlights:

  • Explicit scheduling profiles for managed, on-demand, and Spot capacity
  • ARM64-first capacity strategy
  • Separation between bootstrap/system capacity and workload capacity
  • GitOps-managed NodePools and EC2NodeClasses
  • Deterministic consolidation and disruption policies
  • Documented platform and workload ownership boundaries

Repository: https://github.com/LaurisNeimanis/gitops-karpenter-platform

Autoscaling Platform

Kubernetes · HPA · KEDA · Kyverno · GitOps

A governed autoscaling policy layer where workloads select approved scaling profiles while the platform owns limits, validation, and safety.

Highlights:

  • Platform-owned HPA and KEDA profiles
  • Event-driven scaling and scale-to-zero patterns
  • Kyverno admission-time validation
  • Namespace-level enablement controls
  • Clear separation of autoscaling, capacity, and application concerns
  • Argo CD-managed reconciliation

Repository: https://github.com/LaurisNeimanis/gitops-autoscaling-platform

Observability Stack

Prometheus · Alertmanager · Grafana · Loki · Alloy · Argo CD

A GitOps-managed Kubernetes observability platform focused on metrics, logs, dashboards, alerting, and operational feedback loops.

Highlights:

  • Prometheus Operator with controlled CRD lifecycle
  • Alertmanager routing and noise reduction
  • Loki with object storage
  • Grafana dashboards and data sources
  • Grafana Alloy for log collection
  • Argo CD-managed desired state

Repository: https://github.com/LaurisNeimanis/gitops-observability-stack

Azure AKS Platform

Terraform · Azure · AKS · Networking · Managed Identity

A production-aware AKS infrastructure foundation built around the same ownership and lifecycle principles as the AWS EKS implementation.

Highlights:

  • Explicit Resource Group, VNet, and AKS subnet boundaries
  • AKS with a VMSS-backed system node pool
  • Kubernetes RBAC
  • System-assigned managed identity
  • Azure Blob Terraform backend with native locking
  • CI-enforced formatting, linting, validation, and security checks
  • Infrastructure-only scope with GitOps and workloads managed separately

Repository: https://github.com/LaurisNeimanis/azure-aks-platform

Infrastructure Automation Stack

Terraform · Ansible · AWS · cloud-init · Docker · CI/CD

A production-inspired reference stack showing provisioning, bootstrap, configuration, and application runtime automation in one repository.

Highlights:

  • Modular Terraform with explicit environment separation
  • cloud-init-based bootstrap
  • Automatically generated Ansible inventory
  • Idempotent Ansible roles and templates
  • Docker-based application runtime
  • CI validation for Terraform and Ansible

Repository: https://github.com/LaurisNeimanis/ccore-ai-infra

Application Demo

FastAPI · Streamlit · Docker · GHCR · CI/CD

A small backend/frontend demo used to validate container builds, GHCR publishing, and infrastructure deployment workflows.

Highlights:

  • Independent Dockerfiles for API and UI services
  • GHCR-based multi-architecture image builds
  • Pre-built images pulled by infrastructure automation
  • Clear separation between UI, API, and data concerns

Repository: https://github.com/LaurisNeimanis/ccore-ai-demo


Core Technical Areas

Infrastructure and Cloud

  • AWS VPC, EC2, ECS, EKS, ECR, IAM, RDS/Aurora, ElastiCache/Redis, S3, EFS, SQS, SNS, CloudWatch, ACM, load balancing
  • Azure Resource Groups, Virtual Networks, AKS, Managed Identity, Azure CNI, Azure Load Balancer
  • Hetzner Cloud and Scaleway infrastructure
  • Proxmox, VMware, Citrix XenServer, ZFS, storage, networking, and virtualization

Infrastructure as Code

  • Terraform modules and multi-environment stacks
  • Terragrunt composition and dependency management
  • Remote state, locking, imports, refactoring, drift control, and provider lock discipline
  • CI validation for formatting, linting, security checks, and validation
  • Separation between bootstrap infrastructure and long-lived state

Delivery and Automation

  • GitLab CI/CD, GitHub Actions, and review-driven release workflows
  • Ansible roles, inventories, templates, and idempotent workflows
  • cloud-init bootstrapping
  • Bash and Python automation
  • Docker, Docker Compose, ECR, GHCR, and image delivery workflows

Kubernetes and Platform Engineering

  • Kubernetes, Helm, EKS, AKS, Argo CD, ApplicationSets, and Kustomize
  • Platform/workload ownership boundaries
  • Karpenter capacity management
  • HPA, KEDA, and policy-driven autoscaling
  • RBAC, workload identity, admission policies, and GitOps reconciliation

Reliability, Security, and Operations

  • Metrics, logs, dashboards, alerting, and incident investigation
  • Prometheus, Grafana, Loki, Alloy, CloudWatch, and related observability tooling
  • Root cause analysis and preventive engineering
  • Backup, restore, disaster recovery, and recovery planning
  • Network segmentation, least-privilege access, hardened Linux baselines, and regulated-environment infrastructure controls

Engineering Principles

  • Prefer explicit, understandable systems over clever abstractions.
  • Keep infrastructure, platform, delivery, and workload concerns separate.
  • Design for predictable failure modes and practical recovery paths.
  • Use automation to reduce operational risk, not to hide complexity.
  • Pin dependencies and validate drift where reproducibility matters.
  • Treat observability as a decision-support system, not only monitoring.
  • Optimize for systems that remain maintainable after the original builder leaves.

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Portfolio of production infrastructure and platform engineering work across AWS, Terraform/Terragrunt, ECS, Kubernetes, GitOps, Ansible, and observability.

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