Deep Learning Experts
matched in minutes from over 15,000 CVs with the power of AIHire experts who build and tune neural networks, train computer vision and NLP models, and ship TensorFlow or PyTorch solutions for real products. Get fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts who have recently used Deep Learning
Peter Schillen
Last position:
Senior ML Engineer & AI Researcher at Anonymous client
Project: Defect generation on inspection images of metal surfaces
Environment:* Automated Visual Inspection (AVI), Metallurgy & Manufacturing
Goal & implementation: Concept, architecture, and training of Generative Adversarial Networks (Pix2PixHD / SPADE) for image-to-image transformation. Targeted generation of synthetic material defects (e.g. cracks, inclusions, scale) on rough metal surfaces under real inspection-light conditions for privacy-compliant and efficient dataset expansion (Data Augmentation).
Technical design: Implementation of robust Generative AI and Computer Vision pipelines in Python and PyTorch. Use of semantic segmentation approaches for mask-guided defect synthesis and downstream evaluation with EfficientDet object detection models.
Business impact: Massive dataset upscaling (factor of 10x) without time- and cost-intensive physical inspection runs, while at the same time drastically improving the detection performance of automated inspection systems.
Technologies & skills used: Python | PyTorch | SPADE | Pix2PixHD | EfficientDet | Machine Learning | Semantic Segmentation | Computer Vision
Peter Sachs-Witzel
Last position:
Project Manager for SAP GTS Trade Services, edition for SAP 4/HANA at ETENGO AG
- Creation of detailed concepts for all relevant GTS modules (compliance, pricing, customs clearance, Intrastat, preferences)
- Planning and conducting workshops, including scheduling and documentation
- Development and consulting of the organisational structure and its master data
- Setup and customisation of compliance functions: sanctions list review, embargo, legal control, US re-export
- Uploading and maintaining sanction and commodity lists
- Implementation and validation of integration tests
- Setup and customisation of the modules: pricing (including uploading of customs tariff numbers and STAWN data), customs clearance (including application for test operation with customs), Intrastat and preferences (including preference calculation and LLEs)
- Development and presentation of prototypes
- Derivation of insights for the final concept
Fadi Shoaa
Last position:
Development of a production-ready Enterprise Document AI & Recommendation Platform at Freelancer
- Development of a production-ready Enterprise AI solution for the automated processing of invoices and business documents
- Integration of Azure AI Document Intelligence and LLM technologies into existing business processes
- Development of robust REST APIs for automated document processing and system integration
- Extraction, validation, and storage of structured invoice data in Azure SQL as a base for analytics and machine learning models
- Development of an AI-based recommendation engine with machine learning and deep learning to generate personalized product recommendations based on historical purchase data
- Implementation of logging, monitoring, error handling, and validation mechanisms for stable production use
- Collaboration with business teams to define business rules and integrate the solution into existing enterprise processes
Technologies: Python, Azure AI Document Intelligence, Azure OpenAI, Azure SQL Database, REST APIs, Machine Learning, Deep Learning, OCR, Pandas, JSON, Workflow Automation
Michael Nelz
Last position:
Senior AI Engineer | Forward Deployed Engineer at Tiefbau
- Development of an AI-powered project organization tool for a civil engineering company that intelligently links project, task, tender, schedule, and document data through a knowledge graph.
- Implementation of AI features for document analysis, information extraction, context-based assistance, and voice-based data capture based on Microsoft Azure AI, reducing administrative effort, making information available faster, and supporting project teams in decision-making.
- Tech stack: Python, React, TypeScript, FastAPI, Claude Code, Codex, Graphify, PostgreSQL, Microsoft Azure AI Foundry, Azure OpenAI, Azure AI Speech, Azure AI Document Intelligence, Microsoft Graph, Microsoft Entra ID, Docker, Git, CI/CD.
Karin Albiez
Last position:
AI Benchmark Engineer | Native language specialist German at Lilt
- Task Engineering: Evaluating Coding Agents.
- Asset Creation: Building realistic task environments using datasets and files in German. Crucially, these assets must remain in the target language to genuinely measure multilingual handling.
- Prompting & Translation: finding failure points where AI does not work, in German.
- Implementation & Verification: Supporting the development of robust solutions (reference implementations) and write highly reliable, deterministic verifier scripts (using rubric-based judging only when strictly necessary).
- Calibration & Execution: Analyze execution logs and calibrate task difficulty (Easy to Very Hard) using standard Terminal-Bench run configurations against various model tiers (Haiku, Opus).
- Quality Assurance: Participation in a rigorous, 4-layer human quality control process (creation, human review, calibration review, and audit) alongside automated LLM-based checks to ensure fairness, grammatical accuracy, and benchmark integrity.
- Linguistic Review: Reviewing AI benchmark tasks across Hindi, Arabic, Japanese, Chinese, Czech and Turkish.
Sven Wanner
Last position:
Simulation of Photometric-Stereo Setups at ID Engineering
- Role: Simulation Engineer
- Environment: Mechanical Engineering / Visual Inspection
- Goals & Implementation: Simulation of photometric-stereo setups to determine the best positions for cameras and light sources for each specific part.
- Business Value: Enabled a low-cost and scalable solution for determining part-specific hardware setups.
- Tech Stack: Python, Blender
Philipp Grunert
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
Samuel Kopp
Last position:
Founder & Agentic AI Engineer at Agentakt LLC
Independent engineering practice focused on custom AI systems, production delivery, and fractional technical leadership.
Selected client engagement: Scalutions
Role: Serve as fractional CTO and hands-on technical lead, responsible for the architecture and agentic infrastructure behind its managed B2B outbound operation.
Product: Designed and built OutboundLoop, an agentic SDR operating system for research, qualification, personalized outreach, campaign management, human approvals, measurement, and continuous improvement.
Scope: Own the full system lifecycle—from business processes and agent behavior to context design, model routing, integrations, evaluation, telemetry, reliability, cost control, and production operations.
Anjaneya Marimireddygari
Last position:
Machine Learning Engineer Intern at Slash Mark
- Built and fine-tuned CNN and RNN architectures using transfer learning for real-world classification tasks — core deep learning skills applicable to BMW's multimodal LLM and GenAI vehicle function development.
- Implemented Dropout, Batch Normalisation, and Early Stopping across deep learning experiments; evaluated rigorously using precision, recall, F1-score, and confusion matrices for production-grade reliability.
- Developed an AI-powered attendance management system using LBPH facial recognition, deployed via Flask web interface with real-time SMS notifications — demonstrating end-to-end AI product delivery for real users.
- Collaborated across cross-functional teams to deliver scalable, documented ML pipelines designed for reproducibility — matching BMW's interdisciplinary team and research environment.
- Integrated AI tooling directly into the development workflow from design through to testing, maintaining high velocity without compromising correctness.
Danny-Michael Busch
Last position:
Senior AI Engineer at Just Add AI GmbH
- Automatic detection of content on various documents
- Recommendation Engine
- Dynamic Pricing
Benjamin Matschke
Last position:
Founder, system architect, and main developer at Institute for Artificial Study (IAS)
- Expert-supervised AI systems for scientific reasoning, model evaluation, and research workflows.
- Built the IAS Problem Solver, an orchestrated system for difficult mathematical reasoning; it achieved 84% in one submitted answer set on the Leipzig mathematics benchmark.
- Built a resumable state-machine pipeline for research-grade mathematics benchmark generation: source selection, LLM-agent-based phenomenon discovery, task synthesis, gold-answer and certificate generation and validation, probing, repair, human feedback, and quality gates, targeting tasks that are difficult, natural, verifiable, and cost-effective.
- Current work extends this into budget-aware AI research workflows for real scientific problems with expert review.
Tech stack: Python, OpenAI/OpenRouter-compatible APIs, embeddings, RAG, SQLite.
Lino Giefer
Last position:
Senior Data Scientist at VinFast Germany GmbH
- Led strategic software development of fusion algorithms for precise object tracking, trajectory prediction, and environment modeling based on multimodal sensor data (e.g., camera, LiDAR, radar, GNSS, IMU)
- Developed and implemented navigation algorithms for autonomous vehicles, including path planning, obstacle avoidance, and sensor fusion of visual, inertial, and distance-based sensor sources
- Automated extraction and training processes with CI/CD
- Developed and optimized data pipelines and processes in Microsoft Azure using Apache Spark, Databricks, and PySpark
- Developed and optimized embedded software for automotive control units
- Designed latency-critical software for real-time control in robotic systems with RTOS (freeRTOS, SAFERTOS)
- Used the Vector toolchain (CANdela, DaVinci, CANoe) for configuration and diagnostics
- Optimized existing data pipelines and processes (ETL, data warehouse, SQL)
- Developed and trained machine learning models using PyTorch
- Created deep-learning-based object detection and visual SLAM algorithms, trained on combined data from camera, LiDAR, and IMU sensors
- Implemented computer vision algorithms for object detection and classification in robotic systems using OpenCV and YOLO, utilizing synchronized image and depth data
- Implemented behavior-based control systems for autonomous robots using ROS2 Behavior Trees
- Performed testing, release, and integration of sensor fusion algorithms into automotive production programs
- Ensured adherence to proper software development processes and safety standards to guarantee high data quality (MISRA, ISO 26262, ASPICE)
Mirza Klimenta
Last position:
Agentic AI for a DeepResearch project at Freelance
- Created a multi-agentic system supported by a knowledge graph to automate drafting of research papers
- Used multiple experts (OpenAI models) collaborating during document drafting
- Extracted useful information from the knowledge graph
- Technologies: LangChain, LangGraph, Smolagents, LlamaIndex, dspy
- Infrastructure: Terraform and GitHub Actions (CI/CD) on AWS
- Deployed initial application as a Streamlit app
Shanna Tellaev
Last position:
Problem Resolution Manager at CARIAD SE (VW AG), formerly CARMEQ GmbH (VW AG)
- Automotive SPICE®: all assessments fully achieved
- Agile transformation: V-model → SAFe successfully implemented
- Series release: on-time, quality-assured software delivery for key Volkswagen Group models (including ECE homologation)
- Stakeholder management: internal & external
- Process optimization: implemented a continuous improvement process (CIP) with a tracking system
Sanchit Bhavsar
Last position:
Freelancer at S2S Dynamics UG
- Implementing cross-industry applications with LLMs
- Developing cloud infrastructure for clients
- Implemented end-to-end data pipeline to deploy models in real time
- Managed overall IT system administration and desktop support
Discover over 15,000 top freelancers
Statistics of experts using Deep Learning
Aggregated from the professional profiles of matched freelancers.
Experience
13 years
Position duration
2 years
Positions per freelancer
8
Top business areas
Information Technology, Research and Development, Product Development
Top industries
Information Technology, Education, Automotive
Certification focus areas
Information Technology, Business Intelligence, Research and Development
Bachelor's degree or higher
98%
Master's degree or higher
88%
Doctorate
18%
Certifications per freelancer
2
Most common languages
German, English, French
Speak two or more languages
99%
Based on our profile pool as of 6 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology 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 using Deep Learning
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 6 Sep 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
What it covers
Deep learning is used to build systems that learn patterns from data and improve on tasks such as image recognition, text understanding, forecasting, and anomaly detection. It sits inside machine learning, but usually relies on larger neural networks and more data. Teams bring it in when rule-based logic is not enough.
Common use cases
- Computer vision for inspection, tagging, search, and detection
- NLP for classification, extraction, summarization, and assistants
- Recommendation and ranking for content, products, or leads
- Predictive models for risk, demand, or maintenance
Core stack
Strong professionals often work with TensorFlow, PyTorch, Keras, CUDA, and model-serving tools around them. They also know data pipelines, experiment tracking, GPU training, and evaluation methods that show whether a model is useful in production. The best work connects model design with deployment details.
When freelancers help
Companies often bring in freelance experts when they need fast support for a prototype, a model rewrite, or a hard production issue. That can include dataset cleaning, transfer learning, fine-tuning, hyperparameter search, or moving a model into an existing service. Freelancers are also useful when the internal team lacks deep specialization.
What strong experts do
Strong deep learning professionals do more than train a model once. They can explain data needs, reduce overfitting, compare architectures, and choose the right loss, metrics, and training setup for the problem.
- Work with messy, labeled, or weakly labeled data
- Balance accuracy with latency, cost, and maintainability
- Document trade-offs for product and technical teams
Signals you need help
If training is unstable, results do not generalize, or deployment is too slow, the project needs an expert eye. The same is true when a team is moving from classical machine learning to deep learning, or when a pilot has to become a reliable service. Companies in Germany often ask for remote collaboration first, then on-site work for workshops or sensitive data access.
Frequently asked questions
Curious about Deep Learning? Here are the answers that come up again and again.
Deep Learning is used for image understanding, speech systems, text analytics, recommendations, and other tasks where patterns are complex. It is a fit when the data is large or unstructured and a simpler model is not enough. Many teams use it for both prototypes and production services.
Deep Learning usually learns features automatically with neural networks, while classic machine learning often depends more on hand-built features and simpler models. That makes deep learning stronger for vision, audio, and language tasks, but it can also be harder to train and explain. A good expert will pick the simpler option when it is sufficient.
A strong Deep Learning specialist usually knows PyTorch or TensorFlow, plus data handling tools, experiment tracking, and model-serving basics. For production work, knowledge of GPUs, batching, monitoring, and deployment patterns matters as much as model code. Keras is also common in many stacks.
Deep Learning projects need different levels of depth depending on the scope. A small fine-tuning task may need someone who can adapt an existing model, while a new production system needs a specialist who understands data, training, and deployment. The more uncertain the data and requirements, the more senior the expert should be.
Yes, Deep Learning can still work with limited data when transfer learning, pre-trained models, or strong augmentation are available. That said, the model choice must fit the data size and the problem. In some cases, a lighter approach will be more reliable.
Most Deep Learning work can be done remotely if the data access, security, and communication setup are clear. On-site time can help for workshops, stakeholder alignment, or sensitive environments with restricted data. Many teams use a mix of both.
Look for a Deep Learning expert who talks clearly about data quality, baseline results, evaluation, and failure cases. They should explain why a model choice fits the task and how they will test it in production. Strong work shows trade-offs, not just high-level claims.
A good Deep Learning professional often also knows SQL, Python, data engineering basics, and deployment workflows. For language and vision projects, experience with labeling, feature stores, or vector search can also help. These skills make the model easier to ship and maintain.
The average hourly rate of freelancers who have used Deep Learning in their recent projects is 86 €, which corresponds to a daily rate of about 688 € based on an 8-hour working day.
Of the freelancers who have used Deep Learning in their recent projects, 98% hold at least a Bachelor's degree, 88% hold at least a Master's degree, and 18% hold a doctorate.
On average, freelancers who have used Deep Learning in their recent projects have 13 years of professional experience, with a single engagement typically lasting around 2 years.
The most common languages among freelancers who have used Deep Learning in their recent projects are German (98%), English (98%), and French (21%).
The most common industries among freelancers who have used Deep Learning in their recent projects are Information Technology (84%), Education (56%), and Automotive (41%).
The most common business areas among freelancers who have used Deep Learning in their recent projects are Information Technology (92%), Research and Development (86%), and Product Development (84%).
Main locations of FRATCH Experts, who have recently used Deep Learning
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 
Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
