Infrastructure as Code Experts in Berlin
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Meet FRATCH Experts in Berlin, who have recently used Infrastructure as Code
Deepak Mishra
Last position:
Lead ML Platform Engineer at Billie GmbH
- Mentor team of 6 ML platform engineers through weekly 1:1s, technical design reviews, and best practices, improving team velocity by 35% through structured sprint planning and skill development programs
- Define 2025–2026 ML platform roadmap in collaboration with Data Science, Cloud Engineering, and Product teams, prioritizing automated model governance, cost attribution systems, and multi-environment deployment strategies
- Partner with Data Science, SRE, and Product stakeholders to align ML platform capabilities with business objectives, reducing data scientist deployment friction by 60% through self-service platforms
- Architect and deliver production-grade MLOps platform supporting 50+ models in production with automated promotion pipelines, versioning, and rollback capabilities, achieving 99.5% platform uptime SLA
- Design distributed ML pipeline architecture using Metaflow and Argo Workflows (Vertex Pipelines-compatible), reducing model training time by 30% and deployment cycles from 2 weeks to 3 days through full CI/CD automation
- Build containerized ML services on Kubernetes with auto-scaling policies, resource quotas, and multi-tenancy isolation, optimizing infrastructure costs by $180K annually (25% reduction)
- Implement monitoring, alerting, and performance tracking using Prometheus, Grafana, and custom instrumentation, reducing model debugging time by 50% and establishing model performance SLOs
- Lead development of RAG-based document intelligence platform using LangChain, LangGraph, and vector databases, implementing agentic AI workflows for automated financial document processing
- Implement Infrastructure-as-Code using Terraform for reproducible environment provisioning and GitOps workflows, reducing infrastructure drift incidents by 80%
- Design role-based access control for ML platform, implement model lineage tracking, and establish audit trails for regulatory compliance aligned with enterprise IAM best practices
Jorge Nuricumbo
Last position:
Senior Developer at SafeXSmart KI Solutions UG
AI Platform Backend – Senior Developer
Brought in to design and build a backend for an AI platform from scratch, including multi-provider LLM orchestration and real-time infrastructure for AI influencer personas at scale.
Tasks and responsibilities
- Architected and implemented a multi-LLM orchestration layer with Semantic Kernel to integrate GPT-4 and other providers for core platform logic and AI influencer personas, reducing model-switching overhead by abstracting provider APIs behind a single interface.
- Designed and developed a backend from scratch in C# / .NET 10, including domain modeling with DDD, a versioned RESTful API layer, and cloud infrastructure setup on Azure.
- Built a real-time chat infrastructure with Server-Sent Events (SSE), message persistence, and delivery guarantees for live operation of AI influencer personas at scale.
- Developed a media management service with integration of cloud object storage for upload and retrieval of influencer-generated content.
- Created an integration and unit test suite with data seeding for reliable regression testing across all core platform flows, significantly reducing the production error rate.
Tools and technologies: C#, .NET, ASP.NET Core, Python, TypeScript, MySQL, Semantic Kernel, EF Core, Minimal APIs, LLM Orchestration, Prompt Engineering, Agentic AI, Generative AI, AI-Assisted Engineering, Claude Code, GitHub Copilot, Google Gemini, OpenAI API, Ollama, Redis, Azure, Azure Container Apps, Azure Database for MySQL, Docker, GitHub Actions, Clean Architecture, Vertical Slice Architecture, CQRS, Domain-Driven Design, REST API, xUnit, Integration Testing, Unit Testing, Jira, Confluence, Scrum
Julius Herrera Glomm
Last position:
Freelancer at Freelancer — Pharma Industry
- Led migration to GCP using Terraform, GKE, and GitOps, improving deployment consistency and scalability
- Implemented Datadog observability stack via Terraform and datadog-operator
- Established automated end-to-end tests and on-call processes, improving incident response and service reliability
- Migrated from NGINX Ingress Controller to Kubernetes Gateway API (NGINX Gateway Fabric)
- Migrated stateful services (PostgreSQL and Redis) to GCP, improving scalability and operational reliability
Santhosh Kannan
Last position:
Freelance Software Engineer at Zalando SE
- Support Authorization as a Service initiative for enterprise-scale authorization platform
- Incorporate comprehensive observability solutions into authorization infrastructure
- Provision and manage AWS infrastructure for authorization services
- Mentor development team on AWS and Kubernetes best practices
- Tech Stack: Java/Kotlin, Golang, Python, OPA, Spring Boot, AWS, Kubernetes, Terraform, ELK Stack, Prometheus, Grafana
Sejal Vaidya
Last position:
Data & ML Engineering at Consulting
- Fractional leadership; consulting growth-stage startups and scale-ups on data strategy, ML products, and platform foundations
- Building decisioning systems for growth, personalization, & product experimentation, across e-Commerce, Digital Health, Energy, and Logistics
- Exploring Agentic AI & LLM-based tooling for production readiness patterns
Marina Kornilova
Last position:
Independent Software Developer at LILARAUM
- Independently designed, developed, published, and maintained mobile games for iOS and Android.
- Implemented application architecture, gameplay systems, UI, monetization, analytics, and platform integrations.
- Managed the complete release lifecycle, including testing, store publication, production monitoring, and iterative improvements based on analytics.
Victor Omojoye
Last position:
AI Training Engineer at Confidential AI Research Client
- Codebase Evaluation & Problem Design: Designed and stress-tested complex software engineering problems against large open-source Python codebases (including pandas), requiring deep context acquisition and architectural understanding to produce well-scoped, realistic problem statements aligned to strict correctness guidelines.
- Agent Failure Analysis: Assessed LLM coding agent solutions for correctness and completeness, identifying meaningful failures across edge case handling, dtype behaviour, and multi-column NaN propagation logic; documented findings with precision for downstream evaluation use.
- Programmatic Test Suite Development: Authored comprehensive pytest suites to programmatically verify agent-generated solutions against defined requirements, with deliberate coverage of boundary conditions and failure modes not caught by naive implementations.
- Containerised Environment Engineering: Built and debugged Docker environments for reproducible agent execution, including git-based repository provisioning, dependency pinning with npm ci, and multi-stage Dockerfile authoring across Linux-based containers.
Nune Isabekyan
Last position:
Fractional CTO at OpsWorker
OpsWorker turns Kubernetes alerts into root-cause analyses, on top of the monitoring a team already runs. I lead the technical side: the agent architecture, the AWS infrastructure it runs on (fully inside EU regions), and the engineering decisions behind it, read-only in the cluster by default, human in the loop for judgment. The stack underneath: Amazon Bedrock and Bedrock AgentCore, agents built with the Strands Agents SDK, the Claude and OpenAI APIs, and the Kubernetes API.
Jan Krol
Last position:
Data Expert at Manufacturing
Enrico Goerlitz
Last position:
Freelance Software & Data/AI Engineer at Freiberuflicher Software & Data/AI Engineer
- Lecturer for the GenAI Track at the Master School Institute of Technology
- Development of a full-stack AI application (React + Python/FastAPI) for automated supplier product import with intelligent column and category classification (4-layer hierarchical) including human-in-the-loop validation
Mathias Wilhelm
Last position:
Implementation of an on-premise OCR solution with information extraction at Mindhopper GmbH
- Insurance service provider*
Challenge: Business-critical documents were processed through external OCR providers, with ongoing costs, dependency, and data privacy risks for sensitive insurance data.
Implementation:
- Architecture and production implementation of an on-premise OCR solution with full data ownership
- Methods for recognizing document structures as the basis for automated further processing
- ML-, NLP-, and LLM/VLM-based information extraction, especially from invoices and quotations
Success: Replaced external providers: full data ownership, GDPR-compliant processing, and 75% lower recurring OCR costs per year
Used technologies: Python, Docker, Microservices, FastAPI, PyTorch, Torchvision, MongoDB, MySQL
André Beran
Last position:
External Attack Surface Assessment & Cybersecurity Readiness Checks at Graydaxe Cybersecurity GmbH
- Conducting cybersecurity readiness checks based on an in-house assessment methodology
- Analyzing the external attack surface using the Graydaxe EASM platform
- Assessing maturity levels and deriving prioritized recommendations for action
Sebastian Striebig
Last position:
Group Product Manager – Digital Platform Discovery at SPREAD.AI
- Developed and implemented organization-wide discovery framework based on Ulwick’s Outcome-Driven Innovation; enabled 7 Product Owners to systematically identify and quantify unrealized value through shared outcome language and opportunity scoring methodology
- Transformed Product Owner role from backlog clerks to strategic experimenters; established dedicated time budget for autonomous hypothesis testing and discovery activities
- Rebuilt customer journey maps to start at actual user need (tool selection phase) instead of platform entry point; eliminated manual data aggregation work previously done by project teams
- Implemented OKR framework across 4 product teams; defined quarterly objectives with measurable key results (e.g., 40% reduction in manual integration effort, self-service adoption increase)
- Unified 3 separate platform roadmaps through cross-team dependency mapping and shared service agreements
- Supported enterprise sales cycle with ROI modeling and technical due diligence for automotive and defense customers
Qaiser Abbasi
Last position:
Freelance Lead DevOps Engineer at Schwarz Gruppe Produktion
Bootstrapping a CloudOps team and building a multi-cloud provider backend for a low-code Internal Developer Platform (IDP) with env zero
Introducing user story mapping, ADRs, milestones, and backlog management
Designing and developing core APIs, setting up CI/CD pipelines, OpenTofu/Terraform scripts
Representing and communicating the team with third-party stakeholders (e.g. env zero)
(Cross-)team coaching on DevOps, software design, Terraform, Golang, and agile practices
Ottavio Braun
Last position:
Semantic Test Framework for LLMs
Discover over 15,000 top freelancers
Statistics of experts using Infrastructure as Code
Aggregated from the professional profiles of matched freelancers.
Experience
15 years (Germany: 16 years)
Position duration
2 years (Germany: 1.9 years)
Positions per freelancer
9 (Germany: 12)
Top business areas
Information Technology, Product Development, Project Management
Top industries
Information Technology, Banking and Finance, Retail
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
93% (Germany: 91%)
Master's degree or higher
45% (Germany: 55%)
Doctorate
3% (Germany: 8%)
Certifications per freelancer
3 (Germany: 4)
Most common languages
English, German, Russian
Speak two or more languages
100% (Germany: 98%)
Based on our profile pool as of 30 Aug 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Berlin are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows how many freelancers charge within that range.
Average rates of experts in Berlin using Infrastructure as Code
Rates are based on recent contracts and do not include FRATCH margin.
The average daily rate is the mean of all daily rates from recent contracts of comparable freelancers on our platform.
The median daily rate is the middle value of all daily rates — half of comparable freelancers charge less, half charge more. Unlike the average, it is barely affected by outliers.
Calculated based on our freelancers’ daily rates as of 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
What it does
Infrastructure as Code turns servers, networks, permissions, and cloud services into versioned configuration. Instead of manual setup, teams define infrastructure in files and apply the same pattern again and again. It is used for consistent environments, safer changes, and faster recovery.
Common tools
- Terraform for cloud and multi-cloud provisioning
- AWS CloudFormation for AWS-native stacks
- Ansible for configuration and orchestration
- Pulumi for code-based infrastructure
- Git-based workflows for review and approval
Where it fits
IaC shows up in platform engineering, DevOps work, and cloud migration projects. It is common when teams need dev, test, and production environments to stay aligned. In Berlin, specialists often support product companies, agencies, and SaaS teams that want predictable delivery across hybrid or cloud-first setups.
Why freelancers help
Companies bring in freelance experts when a setup has grown messy, brittle, or undocumented. A strong specialist can create reusable modules, refactor legacy scripts, and introduce guardrails for changes. They also help when a team needs extra capacity for a migration, audit, or release deadline.
Strong profiles
Good professionals understand the cloud provider, but they also think in systems. They write clear modules, handle state carefully, and know how to separate reusable parts from environment-specific values. They can work with security, CI/CD, secrets, and access control without turning the codebase into a tangle.
What to expect
For larger environments, Infrastructure as Code work is rarely just file editing. It often includes reviewing current resources, mapping what should be managed, and deciding what must stay manual for now. In Berlin, remote collaboration is common, but on-site time can help when teams are aligning platform standards or untangling older infrastructure.
Frequently asked questions
Need clarity? These are the questions we hear most often about Infrastructure as Code.
Infrastructure as Code is used to define cloud and server setup in files instead of clicking through consoles. That covers networks, instances, load balancers, permissions, and related services. The main goal is repeatable infrastructure that can be reviewed, versioned, and rebuilt.
Infrastructure as Code is the broader approach; Terraform, Ansible, and AWS CloudFormation are common ways to do it. Terraform is often chosen for provisioning, Ansible for configuration and orchestration, and CloudFormation for AWS-native stacks. Many teams combine them when one tool is not enough.
A strong Infrastructure as Code professional usually understands cloud platforms, networking, IAM, CI/CD, and secrets handling. Git workflows and code review practices matter too, because infrastructure changes need the same discipline as application code. Security awareness is essential when modules touch access and production resources.
For Infrastructure as Code, a clear inventory of what exists and what should change is enough to start. The expert should know the cloud provider, deployment flow, and any constraints around security or compliance. If the current setup is undocumented, a discovery phase is often part of the work.
Yes, Infrastructure as Code work is often remote because the core tasks happen in repositories, cloud consoles, and pipelines. Berlin teams commonly collaborate with specialists who join planning calls, review pull requests, and work inside shared environments. On-site time only becomes useful when the team needs fast alignment on platform standards or legacy systems.
Good Infrastructure as Code deliverables are readable, modular, and safe to change. Look for clear naming, small reusable modules, controlled state handling, and plans that show minimal unwanted drift. Quality also means the setup is documented well enough for another specialist to maintain it.
With Infrastructure as Code, cleanup is often the better first step because many teams already have partial definitions in place. A specialist should assess what can be reused, what should be split into modules, and where manual resources still exist. A full rebuild only makes sense when the current structure is too risky to keep.
A good Infrastructure as Code freelancer will ask which cloud services are in scope, how changes move to production, and who approves them. They should also ask about state storage, secret management, and any naming or compliance rules. Those details shape the design more than the tool choice alone.
The average hourly rate of freelancers in Berlin, Germany who have used Infrastructure as Code in their recent projects is 104 €, which corresponds to a daily rate of about 833 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used Infrastructure as Code in their recent projects, 93% hold at least a Bachelor's degree, 45% hold at least a Master's degree, and 3% hold a doctorate.
On average, freelancers in Berlin, Germany who have used Infrastructure as Code in their recent projects have 15 years of professional experience, with a single engagement typically lasting around 2 years.
The most common languages among freelancers in Berlin, Germany who have used Infrastructure as Code in their recent projects are English (100%), German (97%), and Russian (16%).
The most common industries among freelancers in Berlin, Germany who have used Infrastructure as Code in their recent projects are Information Technology (97%), Banking and Finance (47%), and Retail (44%).
The most common business areas among freelancers in Berlin, Germany who have used Infrastructure as Code in their recent projects are Information Technology (100%), Product Development (81%), and Project Management (63%).
Main locations of FRATCH Experts, who have recently used Infrastructure as Code
Our freelancers and interim experts are at home across the DACH region — available on-site in the major business hubs or fully remote. Choose a location to discover matched specialists, local market insights and up-to-date availability.
Hamburg 
Munich 
Cologne 
Frankfurt Request a free demo
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Philipp Thomaschewski
FRATCH CEO
