Find the perfect AI Engineers in Berlin in minutes from 15,000 CVs with the power of AI
Bring in AI engineers for LLM applications, retrieval-augmented generation, model integration, and production pipelines. Useful when you need someone who can move from proof of concept to stable deployment, work with your product and data teams, and keep systems maintainable. Get fast, precise matching with vetted, available freelancers.
About the role
What they build
AI Engineers design and ship AI features that work in production. They connect models to real products, data, and infrastructure, then keep the system stable after launch.
- LLM-powered product features and assistants
- Retrieval-augmented generation with search and knowledge bases
- Model integration, prompt logic, and tool calling
- Evaluation pipelines, logging, and monitoring
- Deployment support for APIs, services, and internal tools
Core skills
A strong AI engineer knows both software engineering and applied machine learning. They should be able to handle data prep, model selection, testing, and system design without turning the project into a research exercise.
- Python, API design, and backend integration
- Prompting, orchestration, and structured output handling
- Vector databases, embeddings, and search workflows
- Cloud deployment, containers, and CI/CD basics
- Clear communication with product, data, and engineering teams
Typical stack
The stack depends on the use case, but most projects involve Python, an LLM provider or open-source model, and a retrieval layer. Many teams also need help with frameworks such as LangChain or LlamaIndex, plus observability tools for tracing and error analysis.
In Berlin, AI Engineers are often brought into SaaS, mobility, industrial tech, and media teams that need fast product experimentation without slowing down their core engineers.
When to hire
Companies bring in freelance AI Engineers when they need focused delivery for a specific build or release. That is common when internal teams have the product idea but lack the hands-on depth to turn it into a working system.
- You need an AI feature built into an existing product
- Your team needs help choosing a model and architecture
- You want an MVP, pilot, or internal automation use case
- You need support for evaluation, quality checks, or cost control
- You want temporary expertise without adding a full-time hire
What strong work looks like
Good AI Engineers do more than wire up an API. They define the use case clearly, choose simple solutions first, and build with failure modes in mind. They also know when a task needs fine-tuning, when retrieval is enough, and when the product should stay rule-based.
Look for clean code, measurable outputs, and sensible trade-offs. A strong freelancer documents assumptions, tests edge cases, and can explain why a model behaved the way it did.
Working with Berlin teams
Freelance AI Engineers often work with Berlin product teams in English, while some companies expect German in workshops or stakeholder meetings. Remote delivery is common, but on-site sessions help when the work depends on sensitive data, fast discovery, or close alignment with engineering leads.
The best freelancers adapt quickly to your stack and can work alongside backend, data, and product teams without creating extra process.
Meet FRATCH AI Engineers
Aruldass Arulanandu
Full-stack AI Engineer
Last position:
Web Module Lead at Mphasis Limited
- Led the end-to-end delivery of enterprise full-stack web applications by driving requirement analysis, solution design, frontend and backend development, database design, API integration, code reviews, team coordination, Agile execution, CI/CD deployments, production support, performance optimization, security implementation, and stakeholder collaboration to deliver scalable, high-quality software solutions.
Haseeb Zahid
Senior AI Engineer | LLM Engineer | ML Engineer
Last position:
Senior Data Scientist at WPP MEDIA
- Designed and deployed enterprise Retrieval-Augmented Generation (RAG) applications using LangChain, LangGraph, vector databases, embeddings, and open-source LLMs served through vLLM on GCP GPU infrastructure.
- Built agentic AI workflows using LangGraph with planning, reasoning, tool execution, persistent memory, session management, and Human-in-the-Loop approval mechanisms.
- Developed LLM-powered automation systems integrating BigQuery, SQL pipelines, and external advertising APIs including Meta, TikTok, Amazon, Snapchat, Google, and Pinterest, reducing manual operational workflows.
- Architected multi-agent AI systems for enterprise analytics and decision-support workflows, enabling autonomous task execution and intelligent data interactions.
- Implemented retrieval optimization strategies including multi-retriever architectures, semantic search, context optimization, and query improvement techniques, improving response relevance by approximately 40%.
- Engineered structured prompting strategies, function-calling schemas, and validation workflows to improve reliability of multi-step LLM applications.
- Designed scalable AI services using Python, FastAPI, Cloud Run, Pub/Sub, BigQuery, Docker, and cloud-native deployment architectures.
Sunish Bharathan
Technical Program Manager . Engineering Delivery & AI Systems
Last position:
AtlasMind - Production AI assistant for Jira at Mercedes Benz Innovation Labs Gmbh
- Converts natural language into JQL using RAG and pgvector. Returns structured JSON with a query, chart spec, and plain-text answer. A two-stage router answers general questions without touching the JQL pipeline at all.
- Interchangeable LLM backends: Ollama, vLLM, Groq, Anthropic Claude, AWS Bedrock - switchable at runtime, no code changes. Self-healing JQL: on Jira validation failure, feeds error back to LLM, retries up to 4 times. OCI Vault for secrets. Deployed on Oracle Cloud A1 with GPU inference over Tailscale private network. Open source.
Deepak Mishra
Lead ML Platform Engineer
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
Viktor Shcherban
AI Engineer & Full-Stack Developer
Last position:
AI Engineer (Freelance) at Empion
Enterprise AI content categorization and AI-powered web research.
- Built multi-LLM evaluation framework with annotated data
- Iterated LLM error rates based on annotated datasets
- Implemented AI-powered web research pipeline Stack: LLM, evals, OpenRouter, Python, Node.js, TypeScript, React
Geza Lakatos
UX Consultant
Last position:
UX Strategist & AI App Developer (AI-assisted App Strategy) at Own Projects (Nutrycoach.ai, LuxuryBandit)
Conceptualizing and developing AI-powered app MVPs from the first idea to a working product. I combine UX strategy with modern AI and no-code tools — especially Claude and Claude Code, where I work directly in the terminal on real code (React, Vite), complemented by Figma, Bolt.new, and Base44 — to quickly validate and build product ideas. Personal platforms: Nutrycoach.ai (AI nutrition coaching with role-based dashboards for clients, coaches, and admins, AI meal analysis, and a scalable design system) and LuxuryBandit (content/creator app). This led to my own practice 'AI-assisted App Strategy', which combines human creativity, UX logic, and intelligent automation.
Muzamal Ali
Data Scientist | AI Engineer
Last position:
Data Scientist / AI Consultant at HelmX
- Delivered AI and data science solutions, including LLM-based chatbots and data pipelines, improving operational efficiency.
- Collaborated on product features, achieving measurable impact and maintaining strong client relationships.
Victor Omojoye
Senior Software & Security Engineer · Systems Analysis · Automation Architecture
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.
William Nguyen
Senior/Lead Business Analyst & AI Workflow Consultant | Requirements Engineering | BI | Workflow Automation | Claude Code
Last position:
Senior Business Analyst/Requirements Engineer at Finanzen.Net/Finanzen.Zero
- Analysis of complex business processes and end-to-end user journeys in digital product and platform environments
- Gathering, structuring, and prioritizing business and technical requirements (Functional / Non-Functional Requirements)
- Translating business goals into actionable requirements, user stories, and acceptance criteria
- Conducting stakeholder interviews, workshops, and reviews with business teams, IT, UX, and management
- Creating and maintaining requirement artifacts (BRD, FRD, user stories, process models, decision papers)
- Ensuring consistency between business needs, technical implementation, and product vision
- Close collaboration with development teams to clarify business questions during implementation
- Support with impact analyses (A/B tests), change requests, and scope management
- Quality assurance of implemented requirements including acceptance criteria and business testing
- Advising on the further development of product strategy and roadmap structure
- Prioritizing backlog items based on business value
- Defining and sharpening product goals, KPIs, MVP definition, and other success metrics
- Evaluating new features, tools, and initiatives from a user and business perspective
- Facilitating decision-making between business, product, and technology
- Supporting go-to-market considerations and product positioning
- Sparring partner for product and stakeholder decisions at management level
- Dashboard creation, data modeling, BI report administration, and data analysis in Power BI
Janina Peters
Growth Engineer & Growth Manager
Last position:
AI and Automation at A-Leecon GmbH
- Introduction to AI and Automation
- The Automation Project
- Make.com Foundations
- Automation Basics: Data Management
- Document Workflows, Troubleshoot, and Automate Reports
- Digression: Data as the Foundation of AI Systems
- Implementing AI Solutions in Practice
- Digression: Large Language Models (LLMs)
Diogo Soares
Mathematician | Programmer
Last position:
Backend Engineer and AI Orchestrator at Stealth Startup
- Providing freelance software engineering and AI orchestration services for an early-stage startup.
- Designing and coordinating autonomous AI systems capable of executing complex, multi- step workflows.
- Developing customer-facing pilots and proof-of-concept solutions.
- Participating in meetings with customers and investors to support product development and business discussions.
Sven L.
CTO | CIO | AI Product Engineer
Last position:
Group CTO at apo.com Group
- Situation: Inherited a patchwork of aging proprietary systems accumulated over 20+ years — custom-built shop, pharmacy operations, logistics, and product data management. No standardization, no automation, no modern delivery practices.
- Team: Found a centralistic hero culture with one manager handling more than 20 direct reports. Removed the bottleneck, replaced low performers, brought resistant team members back on track or managed them out. Hired fresh talent that brought new energy and capability.
- Delivery: Unified nine brands into a single platform, improving delivery speed by roughly 10x and eliminating cross-brand inconsistencies. Launched a new mobile app that tripled mobile revenue share within 12 months.
- AI: Implemented AI coding tools across development departments. Continuously working on trainings and knowledge sharing for developers. Delivered analytics and agentic automation for customer care inbound emails, and initiated agentic call automation.
- Operations: Introduced automated build and deployment pipelines, initiated cloud migration, and brought the core pharmacy system onto a maintainable, current foundation.
- Governance: Took back project prioritization from ad-hoc business demands by introducing transparent capacity planning — the biggest “no” enforced and the most impactful change for the organization.
Nune Isabekyan
Engineering Leader · Fractional CTO of OpsWorker
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.
Enrico Goerlitz
Data & AI Engineering | Backend Software Development
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
Ibrahim Hilali
Senior Full Stack Engineer | Cloud & AI Agent Engineer
Last position:
Senior Full Stack / AI Engineer at Punktum Digital GmbH
- Context: Healthcare and laboratory teams required faster document analysis, treatment-planning support, and reliable AI workflows for MR/VR-assisted operations.
- Contribution: Built the AI healthcare platform, model/agent workflows, VR-glasses deployment platform, REST APIs, Next.js/React interfaces, and CI/CD pipelines.
- Impact: Delivered a production-ready AI product foundation that improved clinical document review, supported laboratory automation, and made VR fleet deployment manageable across environments.
Tech: TypeScript, Next.js, Node.js, React, Java, Spring Boot, Python, PyTorch, TensorFlow, Docker, PostgreSQL, OpenAPI, GitLab, GitHub Actions.
Discover over 15,000 top freelancers
AI Engineers statistics
Aggregated from the professional profiles of matched freelancers.
Experience
14 years
Position duration
2.2 years
Positions per freelancer
8
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Banking and Finance, Professional Services
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
100%
Master's degree or higher
63%
Doctorate
3%
Certifications per freelancer
2
Most common languages
English, German, French
Speak two or more languages
86%
Daily Rate Distribution
The chart shows how the daily rates of freelancers in this role 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. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Average rates for AI Engineers & Seniority distribution
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.
Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Frequently Asked Questions
Have questions? See our quick guide to FRATCH
A freelance AI Engineer turns an AI use case into working software. That usually means designing the workflow, integrating the model, building retrieval or orchestration layers, and making sure the feature is testable and maintainable. The job is about production delivery, not just model experiments.
Look for solid Python skills, backend integration experience, and hands-on work with LLMs, embeddings, or retrieval systems. Strong candidates can also explain evaluation, monitoring, and failure handling in plain language. If they have only prompt-writing experience, that is usually not enough.
An AI Engineer often focuses on shipping applied AI features inside products, especially LLM-based systems and workflow automation. A machine learning engineer may spend more time on training, model pipelines, or broader ML infrastructure. In many teams the titles overlap, but the project scope should make the difference clear.
A freelancer makes sense when you have a defined AI project, a prototype to launch, or a gap in your team’s experience. That is common when you need speed, flexibility, or specialist know-how for a specific build. If the work is core, ongoing, and broad across the company, a permanent hire may fit better.
Most AI engineering work can be done remotely, especially when the team has clear product goals and a stable stack. On-site time in Berlin helps for discovery workshops, access to sensitive systems, or close collaboration with product and engineering leads. Many projects work best with a mixed setup.
Typical deliverables include a working feature, a service or API, prompt and retrieval logic, evaluation scripts, and documentation. Depending on the project, they may also hand over monitoring setup, test cases, and guidance for future improvements. The output should be usable by your team, not just a demo.
Judge the work by reliability, clarity, and product fit. A strong AI Engineer can explain trade-offs, show how the system is evaluated, and avoid unnecessary complexity. Good signs are clean architecture, thoughtful tests, and practical decisions about model choice and fallback behavior.
Not always. Many Berlin product and tech teams work in English, especially when the project is technical and the stakeholders are international. German can help in workshops, with local business teams, or when the AI system serves German-language users.
The average hourly rate for AI Engineers in Berlin is 93 €, which corresponds to a daily rate of about 743 € based on an 8-hour working day.
Of the freelancers working as AI Engineers in Berlin, 100% hold at least a Bachelor's degree, 63% hold at least a Master's degree, and 3% hold a doctorate.
On average, freelancers working as AI Engineers in Berlin have 14 years of professional experience, with a single engagement typically lasting around 2.2 years.
The most common languages among freelancers working as AI Engineers in Berlin are English (94%), German (91%), and French (11%).
The most common industries among freelancers working as AI Engineers in Berlin are Information Technology (97%), Banking and Finance (43%), and Professional Services (43%).
The most common business areas among freelancers working as AI Engineers in Berlin are Information Technology (97%), Product Development (91%), and Research and Development (69%).
FRATCH AI Engineers main locations
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.
Request a Free Demo
Get in touch with the FRATCH team and we will get back to you within 4 hours.
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We always have the time for a call or email!
Philipp Thomaschewski
FRATCH CEO
