Artificial Neural Network Experts in Berlin
in minutes from over 15,000 CVs with the power of AI.Hire experts who design ANN models, tune training pipelines, and deliver classification, forecasting, and anomaly detection work with clean handover to your team, matched fast from vetted, available freelancers.
Meet FRATCH Experts in Berlin, who have recently used Artificial Neural Network
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
Haseeb Zahid
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.
Raphael Mankopf
Last position:
Founder / Quant Developer at Market Maker
- Crypto quant strategy development, automated trade execution, onchain data client (Ethereum / Solana)
- Data and trade architecture development for liquidity provision
Nooshin Omranian
Last position:
Senior Computational Biologist at Max-Planck-Institute for Molecular Genetics
- Conducting research at the interface of proteomics and artificial intelligence, focusing on the application of machine learning models (e.g., neural networks, clustering algorithms, and feature extraction) to analyze complex biological datasets.
- Developing and teaching AI-based analytical workflows for molecular and proteomic data, integrating tools such as Python (scikit-learn, TensorFlow, Pandas) for predictive modeling and data visualization.
- Collaborating with interdisciplinary teams to explore data-driven hypotheses in molecular genetics and enhance biological interpretation through AI-assisted pattern recognition.
- Implementing automated data processing pipelines to improve reproducibility and FAIR data management in high-throughput experiments.
Felix Brunner
Last position:
Project at Machine status detection in industrial 3D printing based on infrared image data
- Guided systematic data collection and pre-processing for the machine learning algorithms
- Defined the labeling process and implemented an interface to annotate the datasets
- Programmed a visual deep learning algorithm to detect machine pollution in live production
- Implemented data augmentation techniques to deal with machine heterogeneity
- Supplied a containerized model with API endpoints for deployment to the production machines
- Coordinated and represented a five-person project team, prepared presentations and reports
Arju Chaturvedi
Last position:
AI-Powered Resume Evaluator at Personal Portfolio
- Employed advanced natural language processing to analyze resume content, identifying key skills and experience relevant to specific job descriptions.
- Provided actionable recommendations for resume improvement, highlighting content gaps and suggesting optimization strategies.
- Evaluated and enhanced compatibility with Applicant Tracking Systems through keyword analysis and format optimization.
David Dai
Last position:
Policy Specialist & Analyst at Tiktok GmbH / Bytedance Ltd.
- Evaluated pain-points of moderation process & strategy
- Conducted lean moderation process and integration of policy issues to improve core metrics KPI, moderation accuracy and consistency
- Collaborated across functions to handle impactful escalations and maintain platform safety
- Conducted agile mechanisms to prevent harmful content such as hate speech, fake news, misinformation on events like German Election (2025), Olympic Games Paris (2024), and harmful AIGC
- Collaborated with product and data science teams to improve the efficiency and accuracy of AI moderation processes
- Applied techniques including RLHF with high-quality data labeling, prompt engineering, model refinement by translating policy wording into decision trees
- Automated processes with Python coding
- Created SOPs and metrics for model iteration and result tracking
Antonius David
Last position:
Student trainee at EASE at University of Bremen
- Independent review and assessment of assignments in the field of image processing with neural networks and student supervision
- Assisting in the creation and annotation of an image dataset for the semantic segmentation
- Development of a demonstrator for deep learning–based liquid level detection in RGB camera images
Fabian Schulz
Last position:
Founder at eudaiTec GmbH
- Founded eudaiTec UG in 2021 and GmbH in March 2022
- Founded eudaiTec Private Limited, India in 2022
- Architected and developed the serverless and microservices-based application meetUs (React Native, Serverless Framework, AWS Lambda, AWS DynamoDB, AWS SNS & SQS)
- Conducted requirements engineering, pre-prototyping and concept development for the web application Trustbuilder
- Developed the cross-platform app conneo (React Native)
- Provided IT consulting for ERP rollout project management (end-to-end testing)
- Provided IT consulting for infrastructure setup at WvSC
Karthikeyan A
Last position:
Cryptocurrency Price Prediction using Machine Learning Algorithms
- Designed, implemented, and evaluated multiple machine learning models (e.g., regression, time series, neural networks) to forecast cryptocurrency prices, incorporating data preprocessing, feature engineering, and model optimization for improved predictive accuracy.
- Performed in-depth data exploration and visualization on large cryptocurrency datasets, using tools like Python and libraries such as Pandas and Matplotlib to identify trends and patterns.
Gönenç Onay
Last position:
Freelance Data Analyst at D4C-Ai
Discover over 15,000 top freelancers
Statistics of experts using Artificial Neural Network
Aggregated from the professional profiles of matched freelancers.
Experience
12 years (Germany: 16 years)
Position duration
2.1 years (Germany: 2.3 years)
Positions per freelancer
7 (Germany: 10)
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Education, Banking and Finance
Certification focus areas
Business Intelligence, Research and Development, Information Technology
Bachelor's degree or higher
100% (Germany: 98%)
Master's degree or higher
90% (Germany: 86%)
Doctorate
30%
Certifications per freelancer
1 (Germany: 2)
Most common languages
German, English, Spanish
Speak two or more languages
91% (Germany: 97%)
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 Artificial Neural Network
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 is
Artificial Neural Networks, often called ANN or neural networks, are models that learn patterns from data. They are used when rules are hard to write by hand and the system must detect structure, make predictions, or classify inputs.
Where they fit
- Image, text, and speech classification
- Forecasting and signal prediction
- Anomaly and fraud detection
- Ranking, matching, and recommendation logic
They also appear in Berlin teams working on research prototypes, product features, and internal data tools. Strong work starts with the right data shape and a clear target task.
Tooling stack
ANN work usually touches Python, NumPy, pandas, PyTorch, TensorFlow, Keras, and scikit-learn. Depending on the project, experts may also handle experiment tracking, model export, and deployment into existing services.
Good specialists know how to choose activation functions, loss functions, optimizers, and network depth without overbuilding the model. They keep training stable and results explainable enough for the team that has to use them.
When to bring help
Bring in freelance expertise when a model must move from prototype to production, when training is unstable, or when current results do not beat a simpler baseline. It also helps when your team needs a fast review of data quality, feature design, or evaluation logic.
What strong specialists do
Strong ANN professionals do more than tune layers. They question labels, inspect leakage, test baselines, and check whether a neural network is even the right fit.
- Define the task and success metric
- Prepare training, validation, and test data
- Compare ANN results with simpler models
- Document trade-offs and handover steps
Berlin delivery
In Berlin, ANN work often sits close to product, research, and data teams. That can mean remote collaboration across Europe or on-site sessions for workshops, reviews, and stakeholder alignment.
Clear communication matters as much as model quality. The best experts explain limits, show assumptions, and keep the implementation practical for the next person who has to maintain it.
Frequently asked questions
The facts hiring teams ask for most often when it comes to Artificial Neural Network.
An Artificial Neural Network is used when a system has to learn patterns from data instead of following fixed rules. Companies use it for classification, forecasting, anomaly detection, recommendation logic, and many other tasks where examples are available and outcomes can be learned.
ANN is the formal name, while neural network is the common short form people use in practice. Deep learning is a broader term for larger neural network setups with many layers, so not every ANN project is deep learning, but many modern ones are.
A strong Artificial Neural Network can capture complex patterns that simpler models may miss. But tree-based models and linear models are often easier to train, explain, and maintain, so a good specialist should compare them before choosing the final approach.
A good Artificial Neural Network specialist should also know data preparation, feature design, Python, and model evaluation. For production work, deployment basics, experiment tracking, and solid communication with product or data teams matter just as much.
A simple proof of concept may need one focused specialist, while production work usually needs someone who has handled training, validation, and release issues before. For Artificial Neural Network work, the key is not only model training but also judging whether the data and task are suitable.
Most Artificial Neural Network work can be done remotely because data, code, and experiments are digital. In Berlin, on-site time can still help for kickoff workshops, stakeholder reviews, or sessions where teams need quick decisions on data and model scope.
Look for clear explanations of baselines, data quality checks, and evaluation choices. A reliable Artificial Neural Network expert does not only show a working model; they also explain why it works, where it fails, and what would improve it next.
If a project has lots of examples but weak predictions, unstable training, or unclear results, Artificial Neural Network expertise can help. It is also a good time to bring in help when the team has a prototype but needs a cleaner path to production or review.
The average hourly rate of freelancers in Berlin, Germany who have used Artificial Neural Network in their recent projects is 93 €, which corresponds to a daily rate of about 743 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used Artificial Neural Network in their recent projects, 100% hold at least a Bachelor's degree, 90% hold at least a Master's degree, and 30% hold a doctorate.
On average, freelancers in Berlin, Germany who have used Artificial Neural Network in their recent projects have 12 years of professional experience, with a single engagement typically lasting around 2.1 years.
The most common languages among freelancers in Berlin, Germany who have used Artificial Neural Network in their recent projects are German (91%), English (91%), and Spanish (18%).
The most common industries among freelancers in Berlin, Germany who have used Artificial Neural Network in their recent projects are Information Technology (73%), Education (45%), and Banking and Finance (45%).
The most common business areas among freelancers in Berlin, Germany who have used Artificial Neural Network in their recent projects are Information Technology (91%), Business Intelligence (82%), and Product Development (82%).
Main locations of FRATCH Experts, who have recently used Artificial Neural Network
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.
Berlin 
Hamburg Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg 
Munich Request a free demo
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Philipp Thomaschewski
FRATCH CEO
