Hire the Best DevOps Engineers
in Poland

I help growing AI teams and SaaS companies fix AWS issues that slow releases, waste cloud budget, and put production at risk. I don't just "run DevOps". Being Solution Oriented, I find the 20% of infrastructure issues causing 80% of failures and overspending: unstable pipelines, slow builds, noisy deployments, mis-sized compute, always-on non-prod environments, missing alerts, weak access controls, unsafe infrastructure changes, backend bottlenecks, full stack developer handoff issues, and weak collaboration between application and infrastructure teams. You get a clear action plan, quick wins in the first days, and production-safe improvements shipped via Terraform, AWS Terraform, CI/CD, ci cd pipelines, rollback-ready delivery, and practical DevOps Engineer execution using AWS Kubernetes, Microsoft Azure, azure data factory and Google Cloud Platform. What you get from a cloud infrastructure optimization engagement: ✅ 5x faster releases via stable CI/CD, ci cd workflows, and AWS Terraform driven delivery ✅ Up to 40% AWS cost reduction by eliminating waste and rightsizing resources ✅ Safer AWS migrations with minimal downtime and rollback plans ✅ Scalable, production-ready cloud infrastructure for growing SaaS and AI products utilizing AWS Kubernetes ✅ 99.9% uptime mindset: monitoring, backups, health checks, and safe rollouts across platforms ✅ Better AWS backend stability for backend, backend AWS, and API-driven platforms ⚙️ How I work as an AWS DevOps Engineer: 1️⃣ Discovery & Assessment: analyze your current Amazon Web Services, Linux servers, backend, kubernetes, AWS ECS, AWS Lambda, AWS EKS, EKS, Amazon EKS, GitLab, GitHub Actions, AWS Terraform, AWS Kubernetes, and deployment pain points 2️⃣ Plan: define clear scope, milestones, risks, and the fastest first win across your infrastructure 3️⃣ Implement: build and automate with AWS DevOps Engineer best practices, DevOps Engineer delivery standards, cloud engineer ownership, and clean documentation 4️⃣ Verify + handover: release runbook, rollback path tested, monitoring validated, and short documentation for your team I mainly work with AWS, but I can also support mixed-cloud teams where Microsoft Azure pipelines, Azure Terraform, Azure Cloud Engineer workflows, Azure infrastructure, Google Cloud Platform, AWS VMware, or migration planning are part of the environment. My expertise covers devops kubernetes integrating AWS Terraform and AWS Kubernetes into multi-cloud setups. Real examples: 🔹 Case: AWS Production-Safe Cost Optimization ⚠️ Problem: Multiple AWS environments across production, staging, pre-production and development were running 24/7, with several oversized resources. 🛠️ Work: Reviewed AWS usage, rightsized resources, added safe Spot Instances, automated start/stop scheduling and improved cost visibility. 🎯 Result: Reduced AWS infrastructure spend by ~40% with zero customer-facing production impact. 🔹 Case: CI/CD for AWS Lambda & Product Engineering Teams ⚠️ Problem: Teams had no standard way to deploy AWS Lambda services. Each service was configured differently and required DevOps involvement. 🛠️ Work: Built 30+ AWS Lambda CI/CD pipelines for Python and Node.js with tests, SonarCloud checks and Orca Security gates. 🎯 Result: Developers could self-provision new Lambda services from a template in under 5 minutes, reducing DevOps dependency by ~80%. 🔹 Case: AWS Multi-Account Infrastructure with Terraform ⚠️ Problem: Product teams needed isolated non-prod, pre-prod and production environments, but provisioning took ~4 hours manually. 🛠️ Work: Built reusable Terraform modules across multiple AWS accounts and standardized environment provisioning with templates. 🎯 Result: Environment provisioning dropped from ~4 hours to ~20 minutes, helping teams ship and test faster. 🔹 Case: Feature-Branch Environments for Next.js / Node.js ⚠️ Problem: Developers shared one staging environment, causing conflicts and slower review cycles. 🛠️ Work: Built GitLab CI + Terraform automation to create isolated AWS Elastic Beanstalk environments per merge request. 🎯 Result: PR-based environments launched in ~10 minutes, removing staging conflicts and improving feedback loops by ~50%. Additional product infrastructure supported: AWS ECS, EKS, Lambda, Terraform, GitHub Actions, GitLab CI, Azure DevOps, Docker, AWS ECR, CloudFront, S3, RDS, Redshift, Dagster Cloud Hybrid Best fit for teams that need senior DevOps support for a launch, migration, infrastructure stabilization, AWS backend improvement, backend AWS architecture, or ongoing production ownership. I can support your team as an AWS DevOps Engineer, devops engineer, cloud engineer, aws architect, and senior delivery owner across Amazon Web Services, kubernetes, ci/cd, github actions, gitlab, Git, AWS Terraform, AWS Kubernetes, Azure DevOps, GCP DevOps, and terraform-based infrastructure. ☎️ Message me a short description of your current pain for fast Senior AWS DevOps Engineer support.

Are your deployments slow, breaking at the worst moments, or costing more than they should? Is your team spending hours on manual releases instead of shipping features? If your CI/CD pipelines are fragile, your Azure infrastructure is growing out of control, or your DevOps processes exist only on paper, you are dealing with a problem that will not fix itself. I am a DevOps Engineer and Azure Architect with 10+ years of experience helping product teams and growing companies build cloud infrastructure that actually works. Whether it is a startup scaling fast or an enterprise team drowning in legacy pipelines, I come in, assess the real state of things, and build systems that are stable, automated, and cost-efficient. My primary expertise is Microsoft Azure and Azure DevOps, end to end. That means everything from designing multi-stage Azure DevOps pipelines and managing releases without downtime, to setting up Azure DevOps RBAC, securing environments, and integrating Azure DevOps with Kubernetes clusters running on AKS. I have built Azure DevOps workflows for teams of 5 and teams of 150, and the approach scales both ways. Every Azure DevOps implementation I deliver is built around real business goals, not just tooling checkboxes. Beyond Azure DevOps, I work across the full infrastructure stack. I design Cloud Architecture using Terraform for Infrastructure as Code, manage containerized workloads with Kubernetes and Docker, and implement Deployment Automation that removes human error from the release process. For data-heavy projects, I build pipelines with Azure Data Factory to handle ETL, orchestration, and integration between services. For teams building decentralized or blockchain-integrated solutions, I have worked with Azure Blockchain Service as part of broader Microsoft Azure infrastructure setups, combining it with the same security and governance standards applied across all environments. As an Azure Architect, I cover Cloud Computing governance, cost optimization, and FinOps practices, so your Azure bill reflects actual usage, not waste. I apply the same structured thinking to Network Security, Linux and Windows Server environments, and multi-cloud setups when needed. When projects require Amazon Web Services, I bring the same DevOps standards to AWS, including AWS DevOps pipelines, CI/CD automation, and infrastructure managed through Terraform. I also work with Google Cloud Platform for teams running workloads across multiple providers. AI Automation is a growing part of what I do. As more teams start integrating AI into their products and workflows, I help them connect those AI components to production infrastructure properly, with reliable pipelines, automated testing, and deployment flows that treat AI workloads the same way as any other service. I also collaborate closely with full stack developer teams to improve release processes, deployment reliability, and infrastructure scalability, making sure the gap between development and operations is as small as possible. What clients usually need help with: ✔ Stabilizing Azure DevOps pipelines and deployment workflows ✔ Building scalable Kubernetes infrastructure ✔ Migrating legacy infrastructure to Microsoft Azure ✔ Reducing cloud costs and improving DevOps efficiency ✔ Designing secure Cloud Architecture for growing products ✔ Automating deployments with Terraform and CI/CD ✔ Improving monitoring, security, and infrastructure reliability ✔ Implementing AI Automation into existing delivery workflows The full technology range I work with includes: Azure DevOps, Microsoft Azure, Amazon Web Services, Google Cloud Platform, Kubernetes, Docker, Terraform, Ansible, CI/CD, Git, SQL, Python, Linux, Windows Server, Azure Data Factory, Azure Blockchain Service, Cloud Architecture, Deployment Automation, Network Security, and more. Certifications: ✅ 7x Microsoft Azure Certified ✅ Kubernetes Certified ✅ Terraform Certified If your infrastructure needs structure, your pipelines need reliability, or your cloud costs are climbing without clear control, send me a message here on Upwork. Describe the situation and I will come back with a clear breakdown and concrete next steps.

🎁 𝐆𝐄𝐓 𝐘𝐎𝐔𝐑 𝐅𝐑𝐄𝐄 𝐀𝐈 𝐑𝐄𝐀𝐃𝐈𝐍𝐄𝐒𝐒 𝐀𝐔𝐃𝐈𝐓 - send me a message and I'll analyze your stack, data pipelines, and AI use cases in 3-5 days. I work as a Machine Learning Engineer, AI Engineer, DevOps Engineer, and Python Developer delivering production Machine Learning systems and AI solutions using Python, with a strong focus on LLM, RAG systems, Computer Vision, and full MLOps / DevOps infrastructure. I operate with a team of 90+ engineers across Machine Learning, DevOps, and Backend, delivering complex Machine Learning and AI systems end-to-end, from data pipelines to deployed, monitored, and scaled systems in production for US and European clients. I'm a Machine Learning Engineer, AI Engineer, and Python Developer with 10+ years of experience building Machine Learning systems and AI solutions using Python for SaaS, fintech, manufacturing, and enterprise companies. As a Machine Learning Engineer, I combine Python, Deep Learning, NLP, Computer Vision, and LLM technologies to build scalable, production-grade Machine Learning systems that solve real business problems. 💻 As a Machine Learning Engineer, AI Engineer, and RAG Developer, I build RAG systems and Retrieval-Augmented Generation pipelines using Python, vector databases, semantic search, and knowledge retrieval systems integrated into production workflows. As a Machine Learning Engineer working with RAG pipelines, I design systems connected to SQL databases, CRMs, and internal knowledge bases. One Machine Learning-powered RAG system reduced support workload equivalent to 3 full-time employees, cutting response time from hours to seconds. As an AI Agent Developer using LangGraph and CrewAI, I build multi-agent Machine Learning systems where each agent handles retrieval, reasoning, and execution in a single production pipeline. 🤖 As a Machine Learning Engineer, AI Engineer, and LLM Developer, I deliver end-to-end LLM integration using Python and models like GPT-4/5, Claude, LLaMA, and Mistral. I build AI agents, AI copilots, and Machine Learning-driven automation systems integrated into enterprise workflows. As a Machine Learning Engineer, I handle prompt engineering, context engineering, embedding pipelines, vector databases like Pinecone, Weaviate, and Chroma, and optimization of Machine Learning and RAG systems in production environments. 👁️ As a Machine Learning Engineer and Computer Vision Engineer, I develop Computer Vision systems using Python, YOLOv8, Detectron2, and OpenCV for object detection, segmentation, and real-time analytics. I delivered a Machine Learning system that replaced manual inspection in manufacturing and reduced defect escape rate to near zero. I also build Document AI systems using OCR tools like Textract and Google DocAI, including full Machine Learning pipelines for processing noisy and unstructured data. 🧠 As a Machine Learning Engineer and NLP Engineer, I build Machine Learning systems using Python for Named Entity Recognition, text classification, semantic search, multilingual NLP, question-answering systems, sentiment analysis, and topic modeling. I combine traditional Machine Learning approaches with LLM technologies to deliver production-ready language systems. ⚙️ As a Machine Learning Engineer, MLOps Engineer, and DevOps Engineer, I design, deploy, and scale Machine Learning systems in production using Python and cloud infrastructure. I build Machine Learning and AI infrastructure with Kubernetes, Docker, Terraform, Ansible, Jenkins, Kafka, Grafana, Prometheus, and NGINX across AWS, Google Cloud, and Azure. In one DevOps and Machine Learning case, deployments scaled from 1 per month to 120 per month, deployment time decreased by 85%, and infrastructure costs were reduced by 55%. As a Machine Learning Engineer and DevOps Engineer, I don't just build models - I deploy, monitor, optimize, and scale Machine Learning systems in real environments. I build Machine Learning systems using Python, AI solutions, LLM applications, RAG systems, Computer Vision pipelines, NLP systems, and scalable MLOps / DevOps infrastructure that works in production. If you're looking for a Machine Learning Engineer, AI Engineer, Python Developer, or DevOps Engineer who understands Machine Learning systems, DevOps infrastructure, and real business workflows - you're in the right place. 🚀 If you're building or scaling a Machine Learning or AI product and need a Machine Learning Engineer, Python Developer, or DevOps Engineer who can design, build, deploy, and scale real production systems - not just prototypes - I can help. Most clients come when their Machine Learning systems are slow, unstable, or not delivering results. I redesign, optimize, and turn them into scalable Machine Learning and AI systems powered by Python and reliable DevOps infrastructure. 📩 Send me a message with your current setup, and I'll tell you what's missing, what can be improved, and whether your Machine Learning system is ready for prod

I'm an experienced Platform Engineer, Site Reliability Engineer and DevOps Engineer. I have years of experience of managing Linux systems, both in bare-metal as well as in virtualized systems. I have managed up to 400 different machines in production situations. Currently I'm focused in helping people and companies move their systems and workflows away from big-tech, and into self-hosting solutions. Examples of things I can help with: Cloud migration: Escape vendor lock-in and unpredictable bills. I can help you assess what can move off the hyperscalers and execute the migration with minimal disruption to your ops. Infrastructure consulting & audits: A thorough review of your current infrastructure, workflows, and dependencies. You’ll get a clear picture of what’s working, what’s at risk, and a clear roadmap to make your operations leaner, more secure, and sovereign. Managed services & ongoing support: Not every company needs a full-time DevOps/SRE hire. I provide ongoing monitoring, maintenance, and support for your infrastructure. You get the reliability of a dedicated Ops team without the overhead. Team training & workshops: Hands-on workshops and training sessions for you team. I cover self-hosting fundamentals, FOSS tooling, infra ownership, and practical migration strategies. Tailored to your stack and skill level, from devs to decision makers.

I'm Vlad Rybnik, and I'm your guide to the world of reliable cloud platforms. With over 5 years of experience in commercial development and more than 11 years in IT infrastructure, I've learned one thing: stability is not a luxury, but a necessity. I help businesses build, scale, and maintain systems that run like clockwork. My main goal is to give you a solution that not only works but also brings value. Whether it's infrastructure automation, deployment optimization, or backend development, I consistently prioritize efficiency and security. My arsenal: ✔Development: Proficient in Python (FastAPI), PHP (Laravel, WordPress), SQL, and Bash. ✔Databases: Work with PostgreSQL, MySQL, MongoDB, and Redis. ✔Cloud & DevOps: I manage cloud environments (AWS, Azure, GCP) and build CI/CD pipelines using GitHub Actions. ✔Infrastructure: Master of containerization with Docker and Kubernetes, building infrastructure as code with Terraform. ✔Monitoring: Set up Prometheus, Grafana, and CloudWatch to always know what's going on. What really sets me apart is my approach. I'm open to new things, find it easy to get along with the team, and am not afraid of complex challenges.

I am a Senior DevOps Engineer with 10+ years of hands-on experience in architecting, automating, securing, and optimizing mission-critical cloud infrastructure for startups, SaaS platforms, fintech companies, healthcare systems, and enterprise-scale applications. I specialize in building highly available, scalable, secure, and cost-optimized infrastructure across AWS, AZURE, GCP, and Kubernetes environments. I have successfully designed and managed large-scale infrastructures containing hundreds of microservices running on single and multi-cluster Kubernetes environments. My expertise extends beyond traditional DevOps into DevSecOps, Cybersecurity, Compliance, MLOps, AI Infrastructure, and Agentic AI deployment systems. I help businesses with: ✔ Cloud Architecture & Infrastructure Design ✔ Kubernetes Architecture (EKS, GKE, Multi-Cluster, Multi-Tenant) ✔ CI/CD Pipeline Design & Automation ✔ DevSecOps Implementation ✔ Infrastructure as Code (Terraform, Ansible) ✔ MLOps & AI Model Deployment Infrastructure ✔ LLM Infrastructure & Agentic AI Deployment ✔ Security Hardening & Compliance Readiness ✔ Cost Optimization & Performance Tuning ✔ Monitoring, Logging & Incident Response ✔ Disaster Recovery & High Availability Architecture Core Expertise: ☑ AWS Architecture & Operations VPC, ELB/ALB, API Gateway, AWS Lambda, Cognito, EKS, EC2, Route53, RDS, IAM, Auto Scaling, CloudFront, EBS, EFS, CloudWatch, ACM, SES, SQS, SNS, WAF, Security Hub, GuardDuty, Secrets Manager, Organizations, Control Tower ☑ GCP Architecture & Operations VPC, VPC Peering, Shared VPC, Cloud Run, GCE, GKE, CloudSQL, BigQuery, Dataflow, Pub/Sub, Cloud DNS, GCS, IAM, Cloud Armor, Security Command Center, Load Balancing, Artifact Registry, Monitoring ☑ DevSecOps & Cybersecurity IAM Security, Zero Trust Architecture, Vulnerability Management, Container Security, Kubernetes Security, Secret Management, SIEM Integration, WAF, DDoS Protection, Security Audits, CIS Benchmarking, Policy Enforcement, SOC Monitoring ☑ Compliance & Governance HIPAA, GDPR, SOC 2, PCI-DSS, ISO 27001, FinTech Compliance, Healthcare Compliance, Audit Readiness, Security Documentation, Risk Assessment, Governance Controls ☑ Infrastructure as Code Terraform, Terragrunt, Ansible, CloudFormation, Pulumi, Docker ☑ Containerization & Orchestration Kubernetes, Helm, Kustomize, Docker, Docker Swarm, Docker Compose, ArgoCD, GitOps, Service Mesh, Ingress Controllers ☑ CI/CD & Automation GitHub Actions, GitLab CI/CD, Jenkins, CircleCI, Azure DevOps, ArgoCD, Atlantis, SonarQube, Automation Pipelines ☑ MLOps / AI / Agentic AI ML Infrastructure, Model Deployment, GPU Workloads, Kubeflow, ML Pipelines, LLM Deployment, Vector Databases, RAG Infrastructure, AI Agents Deployment, AI Ops, Model Monitoring ☑ Monitoring / Observability Prometheus, Grafana, Datadog, ELK Stack, Sentry, Jaeger, OpenTelemetry, Loki, AlertManager, Incident Management ☑ Linux System Administration Ubuntu, Debian, CentOS, RedHat, System Hardening, Performance Optimization, Shell Scripting ☑ High Performance Web Services Nginx, Apache, Tomcat, Varnish, HAProxy, Load Balancers, Reverse Proxy Optimization ☑ Database & Data Infrastructure PostgreSQL, MySQL, MongoDB, Redis, Cassandra, Elasticsearch, Replica Sets, Performance Tuning, Backup Strategies ☑ Source Control & Collaboration GitHub, GitLab, Bitbucket, Azure DevOps ☑ SSL / CDN / DDoS Mitigation Cloudflare, Let’s Encrypt, AWS Shield, WAF, CDN Optimization Why Clients Hire Me: ✔ Fast problem-solving under pressure ✔ Production-grade infrastructure expertise ✔ Security-first architecture mindset ✔ Strong communication & ownership ✔ Long-term scalable solutions, not temporary fixes ✔ Deep understanding of startup and enterprise environments