Artificial Neural Network Experts
to build reliable AI systems with precise matching and vetted, available freelancersHire experts who design, train and evaluate neural models for image recognition, language processing, forecasting and recommendation systems. Get fast, precise matching with vetted, available freelancers who fit your technical needs.
Meet FRATCH Experts who have recently used Artificial Neural Network
Alwin G.
Last position:
IT Interim Manager & AI Strategist
- Founder of CheironX: AI-supported GRC management (ISO 27001, BSI IT-Grundschutz, TISAX, DORA)
- Strategic focus on Agentic AI and GenAI for modern IT Governance, Risk & Compliance Management
- IT interim management and strategic consulting
Michael S.
Last position:
Data Analyst, Data Scientist at CompuGroup Medical Deutschland AG, docmetric GmbH
Development of AI and traditional models to analyze medical and patient data, including medication analyses, diagnosis analyses, forecasts, procedure analyses, dosage analyses, comorbidity analyses, prescription analyses, patient potential analyses, referral profile analyses, and prescription analyses. Analyses in the field of Real World Evidence.
Healthcare IT, pharmaceutical industry
Tasks, responsibilities:
- Gathering customer requirements
- Planning the subproject
- Designing and defining KPIs
- Designing and developing models and visualizations of results using customer dashboards
- Developing and implementing data warehouse adaptations
- Deriving recommendations for action
Methods, technologies:
- Simulation
- Artificial intelligence
- Python
- R
- SQL
- Microsoft Power BI
- Amazon Web Services
- Elasticsearch
- PostgreSQL
- Databricks
- Multivariate statistics
Gabin Maxime N.
Last position:
Multi-Agent R&D Pipeline (3 Custom Agents) at Independent Project
Claude Code subagents, MCP, Pydantic V2, pytest, bandit
Designed and shipped 3 specialized agents that hand work down a line: a research agent writes a cited implementation spec, a coding agent builds the modular code and its tests, a review agent ranks findings by severity and applies the fixes. Each handoff is a structured document, so no stage depends on another agent's context window.
Connected the research agent to an academic-research MCP server (Semantic Scholar, ArXiv, Hugging Face Hub, citation snowballing) so every reference traces to a tool result rather than the model. Gated commits behind ruff, mypy, pytest and bandit, required human sign-off before installs and commits, and persisted session state on disk so long runs survive a context reset.
Normann G.
Last position:
Managing Director / Technology & AI Consultant at Omnia Kassel UG
Managing Director | Omnia Kassel UG
Corporate management, international business development, technology and innovation management
Corporate management and international business development: Strategic and operational management of a technology-oriented consulting company. International B2B business development, development of new business areas, and establishment and maintenance of industrial, technology, and procurement partnerships.
Technology and innovation management: Identification and evaluation of new technologies with regard to technical feasibility, commercial potential, and practical applications. Development of innovative business models, technology transfer, and support for demanding innovation projects.
Development and project management: Design, planning, and management of technical development, procurement, and customer projects. Technical specifications, international supplier selection, coordination of external development partners, and evaluation and implementation of customer-specific solutions.
AI research, systems development, and technology evaluation: Early recognition of the fundamental technological change brought about by generative AI. For around four years, self-initiated, almost daily intensive research, development, and testing work with leading language models, multimodal AI systems, and different model generations. Independent design and testing of around 200 complex AI workflows, multi-agent methods, and book, proposal, and evaluation agents.
AI-supported customer projects and multimodal production: Practical implementation of proprietary and confidential customer assignments in technical design, project development, proposal preparation, specialist texts, and comprehensive documentation. Development and production of texts, images, animations, videos, speech, and avatar applications. A total of around 750 productive work results from various AI-supported projects.
AI quality assurance, process development, and efficiency improvement: Development of multi-stage, cross-model testing and correction procedures with source comparison, independent review, and systematic error analysis. In own task-specific comparative measurements, reduced selected processing times from eight hours to one hour and improved measured accuracy from about 95 % to up to 99 %. Transfer of the findings into productive work processes and commercially usable customer solutions.
Project-based activity and availability: The entrepreneurial activities are organized on a project basis and are not connected with permanent full-time operational utilization. Therefore, there is substantial time available for external consulting, development, and management assignments, including projects with a high workload.
Mirza K.
Last position:
Agentic Automation and a RAG system
- This project involved extraction of intelligence data to support report writing for a company that provides geopolitical, global, commercial intelligence. The data have been gathered from a number of resources (interview transcripts, online data, internal documents), and then a knowledge base has been build from it. This was the basis of a complex RAG system, that was evaluated against a golden dataset. Agents have been used to find out the contradicting intelligence, the statements supporting each other, and to store back the generated knowledge.
Used: Python, RAG, LangGraph, LangChain, deepeval, MCP
Derek M.
Last position:
AI Automations Manager - Hardware Setup, Automation & Voice Agent at Autohaus Mazda
- Setup of new desktop computers
- Setup of a VPS for automation, databases, and chat interface
- Connection to Azure AI Foundry
- Development of various n8n workflows for email, social media, and document management
- Implementation of a QM system (backups, error handling, HITL)
- Setup and optimization of an inbound Voice Agent
Garima C.
Last position:
IT Program Director at Trax Retail
- Directed global IT teams in implementing cloud-based, AI-driven solutions, aligning with customer requirements through comprehensive data migration strategies
- Led cloud migration initiatives from legacy systems to scalable cloud platforms, streamlining data handling through API-driven processes to enable real-time analysis and reporting
- Enhanced reporting accuracy and customer satisfaction using machine learning and neural networks, achieving 97% classification accuracy
- Advanced projects using augmented reality (AR) to optimise user experience and meet client goals
Murad K.
Last position:
Senior AI/ML Engineer at Quantistry GmbH
- Led end-to-end delivery of ML/AI applications for the QuantistryLab simulation platform, from requirements definition and solution design to development, testing, deployment, and production support, coordinating efforts across back-end, DevOps, and ML teams
- Designed and implemented a compound search system with an open-weights LLM, the Pydantic AI agent framework, and the Model Context Protocol (MCP), exposing it through FastAPI endpoints
- Deployed and configured vLLM-based LLM serving on Kubernetes
- Built CI/CD pipelines and Helm charts to automate deployments across staging and production environments
- Instrumented the search system with OpenTelemetry spans and traces, integrating with the Honeycomb observability platform to monitor component-level latency and functional errors
- Developed a custom evaluation dataset and implemented evaluations for retrieval relevance, answer correctness, tool-call accuracy, and end-to-end task success
- Reduced infrared spectrum prediction time by 83% (from 60 to 10 minutes) by replacing a quantum-mechanical model with a pretrained graph neural network; fine-tuned the model using PyTorch, built and containerized the inference pipeline with Docker, and integrated it into the platform
- Implemented an MLflow model registry with MinIO-backed artifact storage
- Advised sales and science teams on ML/AI applications for prospective client projects, providing technical expertise to shape proposed solutions and prototypes
Technologies: AWS, Hetzner, Kubernetes, Helm, Docker, vLLM, Pydantic AI, MCP, Qwen, Llama 3, MLflow, MinIO, GitHub Actions, Python, FastAPI, OpenTelemetry, Honeycomb, PyTorch, Polars, pytest
Bardiya B.
Last position:
Data Scientist at Rewe Digital GmbH
Statistical Forecasting Algorithm
- Improvement of an statistical probabilistic forecasting algorithm for sales + evaluation
- Migration from R/On-premise to Python/Snowflake
- Productionalization on Snowflake in cooperation with data engineers & DevOps
Monitoring Dashboard
- Data engineering for preparation & provisioning of necessary data/resources on Snowflake
- Development & deployment of a Streamlit dashboard in Snowflake
ML-based Probabilistic Forecasting on Vertex AI
- Development of a ML-based probabilistic forecasting algorithm from scratch
- Implementation of MLOps pipeline in Kubeflow on Google Cloud Vertex AI
Tech Stack: Python, Snowflake/Snowpark, R, Streamlit, Gitlab/Gitlab CICD, Terraform, Google Cloud, Vertex AI (aiplatform SDK, gcloud CLI, feature store, model registry, etc), kubeflow
Philipp G.
Last position:
Data Scientist & ML Engineer at Data-Science Factory GmbH
- Building, implementing and selling automated Data Science solutions such as Scorecard Factory and Forecast Factory
- Implementation of automated end-to-end cloud processes
- Development of LLM and NLP models
- Creation of interactive reports
- Support for national and international large corporations as well as medium-sized companies in implementing ML projects
Laurin H.
Last position:
Software Architect (Freelance) at Care4Sure
- Delivered MVP-focused full-stack architecture for a health-sector client: Vite/React frontend, backend services on Google Cloud Run, and Supabase for database plus IAM/authentication.
- Supported product requirements engineering and prioritized cost-aware workload placement, implementing browser-side/edge computation where feasible before moving logic to backend services.
Serge K.
Last position:
Data Scientist & MLOps (Machine Learning Operations) at REWE Digital GmbH
- It is like a startup within REWE, where we have to build a new forecasting system on Google Cloud Platform from the scratch. Although, officially my role is called MLOps, my actual tasks also include development of data processing pipelines (data engineering) and data scientists tasks such as feature engineering and model trainings.
- GCP: Terraform (tofu), Vertex AI (Kubeflow), Cloud Run, IAM, Google Cloud Storage, BigQuery, Artifact Registry
- Data engineering: Snowflake as the main data warehouse, Terraform, DBT for data model implementations
- CI/CD: GitLab. We have built a CI/CD pipeline that automates deployments of new releases up to production environment
- Development of feature store and data synchronization between environments
- Data science: evaluation of different forecasting models, including CatBoost, Contextual Gamma and Random Forest; implementation of training pipelines using Kubeflow on Vertex AI; scaling and development of an event-driven interface using Kafka as a forecasting service; evaluation of MLFlow for model tracking and model registry
- Implementation of REST API using KeyCloak OAuth2 and OpenID (JWT)
- Monitoring: implementation of dashboards with Grafana, Prometheus, Mimir, Streamlit and Loki to monitor prediction metrics, GCP and Snowflake costs for pipelines, and system metrics
- Development of Copilot Agent Skills
Abdulrahman S.
Last position:
Senior Business Intelligence Analyst at ELEMENT Insurance AG
- AI-Enabled Analytics & Reporting: Lead a 2-person BI team, applying predictive analytics and automation to translate complex business requirements into forward-looking insights, recommendations, and financial reports.
- Advanced Data Visualization: Design and maintain 25+ high-impact Power BI/Tableau dashboards tracking company-wide KPIs and trends, structured to support predictive and automated reporting.
- Web Analytics & Insights: Own GA4 tracking and analysis, driving a 15% increase in conversion insight through deeper customer journey analysis.
- Data Pipeline Optimization: Partner with data engineers to redesign and automate data pipeline workflows, cutting manual reporting time by 20% while safeguarding data accuracy and system uptime.
Alireza Y.
Last position:
Master’s Thesis – Autonomous Railway System at Technische Universität Chemnitz
- Developed a CNN-based pedestrian detection system using LiDAR data
- Created Python scripts for bounding boxes, dataset labeling, and data conversion
- Evaluated model performance on datasets with point clouds
Christian M.
Last position:
Self-employed business consultant at CuriousMinds Unternehmensberatung
- Strategic consulting, technical consulting, interim management, and training
- Management consulting and development of application solutions through interdisciplinary solution approaches
- Employee and team development as well as innovation and communication management
Discover over 15,000 top freelancers
Statistics of experts using Artificial Neural Network
Aggregated from the professional profiles of matched freelancers.
Experience
16 years
Position duration
2.2 years
Positions per freelancer
10
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Education, Automotive
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
99%
Master's degree or higher
88%
Doctorate
30%
Certifications per freelancer
2
Most common languages
English, German, French
Speak two or more languages
98%
Based on our profile pool as of 5 Nov 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology are distributed, based on recent contracts on our platform. Each bar covers a rate range — its height shows the share of experts charging within that range.
Average rates of experts 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 5 Nov 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
Artificial Neural Network experts industry focus
See which industries our matched freelancers work in most often — every figure is calculated live from the freelancers on FRATCH.
- Information Technology (83%)
- Education (59%)
- Automotive (52%)
- Manufacturing (43%)
- Healthcare (36%)
- Professional Services (35%)
- Banking and Finance (33%)
- Energy (30%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What Artificial Neural Networks Do
An Artificial Neural Network is a machine learning model made of connected layers that learn patterns from data. It can classify images, understand text, detect anomalies, predict demand and rank recommendations. The model adjusts its internal weights during training rather than relying only on hand-written rules.
Common Applications
Neural networks appear in products and internal systems across many industries. Companies use them when relationships in the data are complex, high-dimensional or difficult to describe with fixed logic.
- Image, video and speech recognition
- Natural language processing and document classification
- Forecasting, scoring and recommendation engines
- Fraud detection and predictive maintenance
Ecosystem and Tooling
Professionals may work with Python, PyTorch, TensorFlow, Keras or JAX, supported by NumPy, pandas and scikit-learn for data preparation and evaluation. Production work can involve GPUs, cloud training services, Docker, model registries and inference APIs. Knowledge of SQL, data pipelines and deployment practices is often just as important as model design.
When Companies Need Specialists
Freelance expertise is useful when a team needs to validate a model idea, improve accuracy, prepare training data or move a prototype into production. It also helps when internal teams need focused support for a new computer vision, language or forecasting capability.
- Training data is inconsistent, sparse or poorly labeled
- A prototype does not perform reliably on new data
- Inference is too slow or expensive for production
- The team needs monitoring, retraining or model documentation
What Strong Professionals Deliver
Strong professionals define the business target and the right evaluation method before selecting an architecture. They establish useful baselines, prevent data leakage and test performance across meaningful segments. They also explain trade-offs between accuracy, latency, compute use, interpretability and maintenance.
Choosing the Right Approach
A neural network is not automatically the best solution. Traditional machine learning may be more suitable for small structured datasets, while rules or statistical models can be easier to audit. The right professional compares alternatives, selects a model that fits the available data and leaves behind reproducible training, clear documentation and a practical deployment path.
Frequently asked questions
The facts hiring teams ask for most often when it comes to Artificial Neural Network.
An Artificial Neural Network is used to learn patterns from examples and produce predictions or classifications. Common applications include image and speech recognition, text analysis, demand forecasting, recommendation systems, anomaly detection and automated scoring.
An Artificial Neural Network can learn complex representations from large or unstructured datasets, such as images, audio and text. Traditional machine learning methods may be easier to train, explain and maintain when data is limited or mostly tabular, so a strong professional should compare both approaches.
An Artificial Neural Network specialist should understand data preparation, statistics, model evaluation and experiment tracking. Useful adjacent skills include Python, PyTorch or TensorFlow, SQL, cloud infrastructure, GPU workflows, APIs, Docker and monitoring for deployed models.
An Artificial Neural Network project may need focused expertise for a proof of concept, but production work usually demands broader experience. Look for someone who has handled data quality, validation, model deployment, performance monitoring and retraining, not only model training notebooks.
An Artificial Neural Network project is often suitable for remote collaboration because code, datasets, experiments and cloud environments can be shared securely. On-site work may help when sensitive data, physical devices or close cooperation with domain teams are central to the project.
A strong Artificial Neural Network professional explains the baseline, data split, evaluation metrics and failure cases clearly. Ask how they prevented leakage, tested generalization, measured inference costs and documented decisions for people who will maintain the system.
An Artificial Neural Network is a good candidate when the task involves complex signals, such as visual content, language or audio, and enough representative data is available. Rules or statistical methods may be preferable when requirements are simple, data is scarce or every decision must be easy to inspect.
An Artificial Neural Network freelancer should provide prepared data workflows, reproducible training code, evaluation results and a clear model artifact. Depending on the scope, deliverables can also include an inference service, deployment configuration, monitoring guidance, documentation and a plan for future retraining.
The average hourly rate of freelancers who have used Artificial Neural Network in their recent projects is 92 €, which corresponds to a daily rate of about 737 € based on an 8-hour working day.
Of the freelancers who have used Artificial Neural Network in their recent projects, 99% hold at least a Bachelor's degree, 88% hold at least a Master's degree, and 30% hold a doctorate.
On average, freelancers who have used Artificial Neural Network in their recent projects have 16 years of professional experience, with a single engagement typically lasting around 2.2 years.
The most common languages among freelancers who have used Artificial Neural Network in their recent projects are English (99%), German (94%), and French (28%).
The most common industries among freelancers who have used Artificial Neural Network in their recent projects are Information Technology (83%), Education (59%), and Automotive (52%).
The most common business areas among freelancers who have used Artificial Neural Network in their recent projects are Information Technology (95%), Product Development (88%), and Research and Development (80%).
Main locations of FRATCH Experts, who have recently used Artificial Neural Network
Our freelancers and interim experts are at home all over Germany — available on-site in Berlin, Hamburg, Munich and every major business hub, or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.
Berlin 
Munich 
Frankfurt In Austria our freelancers and interim experts support companies from Vienna to Graz — on-site where your project needs them, or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.
Vienna Graz Across Switzerland our specialists are active in Zurich, Geneva, Basel and Bern — working on-site or fully remote. Choose a city to discover matched specialists, local market insights and up-to-date availability.
Zurich Geneva Basel Bern Countries:
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FRATCH CEO
