Machine Learning Engineers in Germany
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Meet FRATCH Machine Learning Engineers in Germany
Michael Nelz
Last position:
Senior ML Engineer, AI Engineer at Lanxess AG
- Deployment and scaling of existing ML initiatives, including demand and cash flow forecasts.
- Building robust monitoring with mlflow for data stability, model performance, and drift detection, as well as implementing additional ML use cases.
- Further development of an Agentic AI chatbot for transparent and easy-to-understand model explanations.
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
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
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)
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.
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
Cris Lovell-Smith
Last position:
Head of AI at Harvest Hub
- Leading AI development for aquaculture startup, optimising shellfish visual assessments with machine learning and computer vision.
- Development and systematic evaluation of ML/CV algorithms for shellfish condition and morphometrics, using Python, Pytorch and MLFlow.
- Analysis of model performance, including identification of failure modes and edge cases in production deployments.
- Design of annotation strategies and refinement of labelled datasets for computer vision tasks.
- Detailed analysis of system performance and communication of findings through publication-quality technical reports to investors and fellow R&D staff.
- Responsible for delivery of technical roadmap.
Thomas Hoefkens
Last position:
Senior MLOps, DevOps Engineer at Trianel Energy
- Built and operated an end-to-end MLOps platform on Azure ML and Kubernetes (Kubeflow) for automated deployment, monitoring, and scaling of forecasting models (including Temporal Fusion Transformer, Informer, and Autoformer).
- Implemented CI/CD pipelines in Azure DevOps for the full ML lifecycle – from resource provisioning (Terraform) and data transformation (Hugging Face Datasets, Pandas, PyTorch, CUDA cluster) to training and evaluation, model registry, and endpoint deployment.
- Integrated MLflow for experiment tracking, model versioning, performance monitoring, and automated registration in the Azure Model Registry.
- Developed and containerized PyTorch training jobs (Azure Notebook, Jupyter Notebooks) for price and time series forecasting (PFC models) with automatic rollout via Azure ML Endpoints and REST/gRPC interfaces, Docker containerization, secured with OAuth 2.0.
- Set up monitoring and alerting mechanisms (Prometheus, MLflow Metrics), centralized logging, and cost monitoring.
- Automated infrastructure provisioning and model deployment using Terraform, Helm, and Azure CLI; connected to existing market data systems and event pipelines.
- Migrated existing workloads and databases (IONOS → Azure, MongoDB) and integrated them into central MLOps workflows and internal networks.
- Extended the platform with LLM-based tools (LangChain, LangServe) to integrate GPT-based analysis modules into existing Spring Boot services for market anomaly detection and automated reports.
- Analyzed and designed a software solution to efficiently process large volumes of data (>3000 messages/sec) (market data store).
- Developed Spring Boot / Java 21 container services with RabbitMQ to distribute exchange data through MongoDB (Kubernetes), with fast storage of data in Redis RMaps, deduplication, forwarding messages to Read Model queues, and building Read Models for UI display in MongoDB.
- Integrated RESTHeart to create a REST API for MongoDB.
- Built an Angular frontend to simplify data queries and master data maintenance.
- Agentic coding with remote and local LLMs (Claude Sonnet, Ollama Qwen) and MCP servers.
- Developed Python scripts to transform and clean incoming exchange data (Pandas, scikit-learn).
Kartik Trivedi
Last position:
Master Thesis Student at Fraunhofer LBF
- Topic: Object Detection and Semantic Segmentation for (AUV) Systems using Transformer-Based Vision Models and Sensor Fusion.
- Designed and implemented an end-to-end multi-sensor fusion perception pipeline (Camera, LiDAR, IMU) in ROS
- Developed CNN-based Machine Learning model (YOLOv8) and Transformer-based vision models for real-time object detection
- Processed and clustered 3D LiDAR point clouds using DBSCAN, RANSAC, and voxel grid filtering to enable robust object localisation in noisy environments.
- Designed Bayesian Network models (GeNle) for probabilistic reasoning and sensor-level decision fusion under uncertainty.
- Applied Kalman filtering for sensor state estimation, temporal alignment, and smooth object tracking, reducing false positives in safety-critical scenarios.
- Evaluated system performance under realistic driving dynamics, improving tracking stability and overall perception robustness.
- Built deep learning pipelines for training, validation, and performance evaluation of perception models using sensor data.
Amr Amer
Last position:
Machine Learning Engineer at German Research Center for Artificial Intelligence (DFKI)
- Developed end-to-end reproducible ML pipelines (PyTorch) with data versioning (DVC), experiment tracking (MLflow), automated testing (PyTest), and CI/CD across all training workflows.
- Scaled Vision Transformer and CNN training across NVIDIA A100 GPU clusters (CUDA, DDP, SLURM); applied hyperparameter optimization (W&B Sweeps) to reduce training overhead and identify optimal configurations.
- Developed a real-time 3D human motion generation system (ViT, VQ-VAE, SMPL-X/PIXIE) for personality-conditioned avatar synthesis; achieved state-of-the-art FID = 6.15 and P-FID = 10.31 on the UDIVA benchmark.
- Validated model expressiveness through structured user studies, achieving 86% accuracy in distinguishing extroverted vs. introverted avatar behaviors.
- Optimized inference pipelines by deploying PyTorch models via TensorRT and ONNX Runtime into native C++ code; benchmarked performance.
Deepak Reddy Narra
Last position:
Machine Learning Engineer at go AVA GmbH
- Designed and built a multi-tenant Python/Flask API platform with JWT + API-key authentication, scoped access control, and service-level orchestration as the backbone for AI applications.
- Built a multimodal RAG system with hybrid chunking, dense/sparse embeddings, hybrid retrieval, reranking, and vector search to deliver grounded, high-precision responses across enterprise data.
- Productionized AI workflows with Docker, CI/CD, Redis-backed async job tracking, webhook callbacks, external AI/media service integrations, and runtime health/reliability controls.
Farzad Ziaie Nezhad
Last position:
Markerless 3D Pose Estimation
- Developed a deep learning system with multi-view Basler cameras for markerless 3D pose estimation
Katharina Schmidt
Last position:
Virtual staining at Faculty of Electrical and Computer Engineering, TU Dresden
- Technical and professional management of software and ML development; largely independent implementation of programming and guidance of the team and external project partners
- Design, creation, and preparation of training and test data sets from experimental image data and simulations
- Selection, implementation, training, validation, and testing of neural networks for image-based reconstruction and transformation
- Systematic evaluation, comparison, and optimization of various model architectures (convolutional neural networks, generative adversarial networks, autoencoders, transformers)
- Design and implementation of explainable AI analyses for model interpretability and robustness assessment (analysis of feature maps, augmentation studies, guided backpropagation)
- Presentation of the developed methods and results in project meetings and at international conferences
Louis Guitton
Last position:
Freelance Solutions Architect and Machine Learning Engineer at Self-employed
- Develop and demonstrate solutions using GenAI software like langchain, vercel ai sdk, copilotkit
- Work with customers to understand their challenges and provide the best solutions based on open-source data products
- Build RAG and GraphRAG solutions using Neo4j, lancedb, and Postgres
- Deploy a LLMOps platform using kubernetes, terraform, helmfile, Arize phoenix, mlflow
- Architect and build data pipelines using dbt, Trino, Spark, Iceberg, Airflow, ArgoCD, terraform, kubernetes
- Delivered user-centred technical strategy for Agriculture 4.0 and precision livestock farming, helping my client secure funding from Bpifrance
- Delivered a prospecting tool for a leading French solar carport installer, using geospatial computing (GIS), speeding up the sales process
- Built digital twin architecture for solar carports and EV chargers, making real-time monitoring and smart charging possible
Julien Look
Last position:
MLOps Engineer at SAMGEN
- Building and scaling cloud infrastructure on GCP to support a SaaS platform for industrial clients
- Designing and implementing a data-driven DevOps pipeline for streamlined deployment and CI/CD workflows
- Collaborating with Data Science team on MLOps workflow to automate integrated retraining
Discover over 15,000 top freelancers
Machine Learning Engineers statistics
Aggregated from the professional profiles of matched freelancers.
Experience
12 years
Position duration
2.1 years
Positions per freelancer
7
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Education, Professional Services
Certification focus areas
Information Technology, Business Intelligence, Research and Development
Bachelor's degree or higher
97%
Master's degree or higher
86%
Doctorate
11%
Certifications per freelancer
2
Most common languages
German, English, French
Speak two or more languages
92%
Based on our profile pool as of 26 Aug 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this role in Germany 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 for Machine Learning Engineers in Germany
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 26 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the role
What they build
Machine learning engineers turn data into production systems. They do not stop at notebooks. They build, train, test, and deploy models that can run in real products and business workflows.
- Model pipelines for training, evaluation, and deployment
- APIs and services that expose predictions to other teams
- Monitoring for drift, quality, latency, and failures
- Reproducible experiments and clear handover for product teams
Core stack
A strong machine learning engineer works across Python, SQL, and common ML frameworks such as PyTorch, TensorFlow, and scikit-learn. They also know how to work with cloud services, containers, feature stores, and CI/CD setups.
They should understand data engineering basics, model versioning, and how to move work from research to production without breaking the system. In Germany, this often means working with product, data, and platform teams in parallel, sometimes in English, sometimes in German.
Typical use cases
Companies bring in a machine learning engineer when a model is ready for real use, but the team lacks the production skills to ship it safely. That includes recommendation systems, fraud detection, demand forecasting, ranking, anomaly detection, and NLP features.
- Turn an offline prototype into a stable service
- Improve an existing model that is slow, brittle, or hard to maintain
- Set up training and deployment workflows
- Connect data sources, model outputs, and business tools
What strong freelancers do
Top freelancers do more than code. They ask about data quality, business goals, deployment limits, and who will own the system after launch. They write clean, testable code and make trade-offs visible.
They also know when a data scientist, software engineer, or MLOps specialist is needed next. That matters when the project mixes model work with backend integration, cloud infrastructure, or operational support.
Tools and methods
The best machine learning engineers can work in modern stacks without needing a long ramp-up. They are comfortable with notebook-based exploration, then hardening the solution for production.
- Python, SQL, Git, Docker, and Linux
- PyTorch, TensorFlow, scikit-learn, MLflow, and Airflow
- AWS, Azure, or Google Cloud
- REST APIs, batch jobs, and model monitoring
When freelance makes sense
Freelance support is a good fit when a company needs focused delivery, not a long hiring process. It also helps when a project has a clear scope, a fixed deadline, or a gap in the team’s machine learning and deployment skills.
For companies in Germany, freelancers are often useful when local stakeholders need on-site workshops but the build itself can happen remotely. That mix works well for product teams, industrial companies, insurers, and software firms that need practical execution, not research-only work.
Frequently asked questions
Quick answers to the questions that come up most around Machine Learning Engineers.
A Machine Learning Engineer builds models that can run in production, not just in a notebook. The work usually covers data preparation, training, evaluation, deployment, and monitoring. In practice, that can mean an API, a batch scoring job, or a full ML pipeline tied to a product.
Look for strong Python and SQL skills, plus hands-on experience with frameworks such as PyTorch, TensorFlow, or scikit-learn. A good freelancer also understands cloud setups, containers, testing, and model operations. The best candidates can explain trade-offs in data quality, latency, and maintainability.
A Machine Learning Engineer focuses on building and shipping models into real systems. A data scientist is often more focused on analysis, experimentation, and insight generation, while an MLOps engineer may focus more on deployment infrastructure and operations. In smaller teams, one freelancer may cover parts of all three.
Freelance support makes sense when you need specialist execution for a defined project or a short-term capability gap. It is also useful when a team already has data science or backend talent, but lacks production ML experience. That keeps delivery moving without adding a long permanent hiring cycle.
Most Machine Learning Engineers can work remotely for the build, review, and deployment work. On-site time helps when there are workshops with product, data, or leadership teams, especially if the project needs close alignment on business rules or data access. Many German companies use a hybrid setup for that reason.
Expect code, not just slides. A strong freelancer should leave behind a working training pipeline, a deployable model service or batch process, evaluation results, and clear documentation. They should also make it easy for your team to maintain the solution after handover.
Judge quality by production readiness, not only model accuracy. Good work is stable, reproducible, monitored, and integrated with your systems. If the freelancer can explain failure modes, data assumptions, and rollback options clearly, that is a strong sign.
German companies in manufacturing, mobility, retail, insurance, finance, and software often need this role. They use it for forecasting, quality checks, personalization, risk scoring, and process automation. The best freelancers can adapt to industry data and work with both technical and business teams.
The average hourly rate for Machine Learning Engineers in Germany is 89 €, which corresponds to a daily rate of about 708 € based on an 8-hour working day.
Of the freelancers working as Machine Learning Engineers in Germany, 97% hold at least a Bachelor's degree, 86% hold at least a Master's degree, and 11% hold a doctorate.
On average, freelancers working as Machine Learning Engineers in Germany have 12 years of professional experience, with a single engagement typically lasting around 2.1 years.
The most common languages among freelancers working as Machine Learning Engineers in Germany are German (97%), English (95%), and French (21%).
The most common industries among freelancers working as Machine Learning Engineers in Germany are Information Technology (87%), Education (49%), and Professional Services (38%).
The most common business areas among freelancers working as Machine Learning Engineers in Germany are Information Technology (97%), Product Development (85%), and Research and Development (74%).
FRATCH Machine Learning 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.
Berlin 
Munich 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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Philipp Thomaschewski
FRATCH CEO
