LSTM Experts in Munich
matched in minutes from over 15,000 CVs with the power of AIWork with specialists who build robust recurrent neural network architectures, train predictive sequence models, and deploy time-series forecasting pipelines through fast, precise matching with vetted, available freelancers.
Meet FRATCH Experts in Munich, who have recently used LSTM
Xinyang M.
Last position:
Sales Operations Analyst Intern at Capgemini
- Developed and maintained 5 Power BI dashboards for pipeline tracking, forecasting, revenue-gap, quota-achievement, and deal-performance analysis.
- Delivered weekly, monthly, and quarterly reporting used by approximately 100 stakeholders across Sales, Finance, and Marketing.
- Built semantic data models and ETL workflows using Power Query, DAX, and SQL; integrated Salesforce, SharePoint, internal data warehouse, and Excel sources.
- Automated data ingestion, cleaning, transformation, format standardization, KPI calculations, dashboard refresh, and reporting preparation using Power Query, DAX, and Power BI, eliminating several hours of recurring manual data preparation and reporting work.
- Standardized KPI calculations and built interactive reports with row-level security, drill-through, and Waterfall analysis for business reviews and forecasting.
Raghu Ram V.
Last position:
Telco Customer Churn Prediction – End-to-End ML Pipeline at Self-Initiated Project
- Designed and implemented a full machine learning pipeline for churn prediction using the Telco dataset.
- Applied preprocessing techniques including missing value handling, categorical encoding, feature scaling, and PCA.
- Built and compared over 15 models (logistic regression, random forest, XGBoost, etc.) and evaluated them using accuracy, precision, recall, F1 score, ROC AUC, and PR AUC.
- Tuned hyperparameters with GridSearchCV, achieving 80.6% accuracy with random forest and XGBoost.
- Created visual reports (bar plots, heatmaps, radar charts) to interpret model performance and churn drivers.
- Exported reusable pipelines and trained models with joblib for deployment.
Anton K.
Last position:
Head of Overall Technical Integration NSC / Hadoop Cloud Development at IABG
Head of overall technical integration NSC (National Secure Cloud, project with approx. 60 employees).
Technical integration of all subprojects into one product, definition of interfaces and basic components of a cloud including hardware, technical architecture of the IABG platform.
Development of a Cloud Management Platform (CMP) capable of creating private/mixed clouds of any complexity based on a textual description with one click or interactively.
CMP also includes the complete hardware management lifecycle.
Kubernetes, OpenStack and Hadoop are used as the foundation.
The management layer includes Harbor, Gitea, Longhorn, Keycloak, Rancher and Jenkins, which are configured automatically.
Private cloud can run any customer workloads, including a full Hadoop layer with HDFS, Spark, MapReduce, Mesos, HBase and around 20 additional ML/DL technologies.
Hadoop worker clusters can also be installed automatically without Kubernetes on bare metal or commodity hardware.
OpenStack with Nova, Neutron, Ironic, Swift, Cinder, Ceph.
Development of a Java application Rudi: SOAP, REST, containers, DB.
Technologies: Kubernetes (K3s, Rke2, Minikube, Harbor, Gitea, Jenkins, Longhorn, Keycloak, Rancher), OpenStack (Nova, Neutron, Keystone, Swift, Ceph, Cinder, Sahara, Magnum, Kayobe, Kolla, Bigrost, Ironic), Hadoop (HDFS, Ambari, Solr, Livy, Ranger, YARN, Tez, HBase, Kafka, Hive, Zookeeper, MapReduce, Spark, Oozie, Flink), virtualization (Kubernetes (K3S), VMware, Oracle), scripting (Ansible, Puppet, Juju, Shell, Groovy, Gradle, Maven).
Borui L.
Last position:
Spectral Analysis of Neural Network Kernels at Borui Li Projects
- Explored the impact of neural network structure on network-inspired kernels, such as Neural Tangent Kernel (NTK).
- Demonstrated through theoretical analysis and empirical studies that the RKHS of NNGP is a subspace of NTK.
- Explored the connections between these kernels and the Matérn family.
Himanshu N.
Last position:
Principal (Data Scientist/Data Engineer/Gen AI Engineer) at Marktguru Deutschland GmbH
Architected an agentic, real-time offer orchestration engine where specialized agents (retrieval, pricing/optimization, and policy/guardrails) coordinate to personalise promotions across customer touchpoints using RAG with FAISS over Delta Lake and low-latency Databricks Model Serving. Collaborated with product managers and commercial stakeholders to shape the roadmap and evaluate emerging agent patterns for production.
Designed an agent-based data quality service that orchestrates schema detection, entity normalization, and validator/exception-handling agents to clean multi-retailer SKU feeds at scale. Wrapped model calls in PySpark UDFs for distributed inference, automated via Databricks Workflows and CI/CD.
Developed a multimodal, agentic extraction pipeline where vision, parsing, and compliance agents collaborate to derive brand, packaging, and volume from scanned images using Claude 3 Sonnet with Swin Transformer encoders. Orchestrated via Azure Event Hub with outputs persisted to Delta Lake.
Implemented a GS1 taxonomy classification service built around cooperating agents for inference, drift monitoring, and auto-retraining governance using Falcon 180B (LoRA-tuned) with a batch pipeline on Databricks.
Created a hybrid agent workflow where a retrieval agent surfaces candidate matches via embeddings and a reasoning/verification agent (Mixtral 8x7B) adjudicates receipt-to-SKU alignment, integrated into a streaming Databricks pipeline.
Built a multimodal attribute inference pipeline structured as cooperating vision-language, rules/consistency, and compliance agents to fill NutriScore, nutrition fields, and packaging types from names and images using LLaMA 3-8B with CLIP embeddings.
Developed a GenAI-powered orchestration system that ingests recipes from multiple websites, parses ingredients through structured extraction agents, and dynamically links them to real-time retailer offers via tagging, semantic reasoning, and business-rule agents.
Daniel C.
Last position:
Founder & Managing Director at BotCraft GmbH
- Building the company with a focus on connectivity for IIoT and Industry 4.0, iRPA/process automation, advanced robotics and smart systems, sensors and services
- Project management and software architecture for IoT gateway development (since 2020) with protocol translation, IT/OT convergence and GRC
- Developing RPA bots for automating and monitoring industrial processes with an agent-based AI approach (since 2020)
- Implementing unsupervised clustering and anomaly detection for time series data in big data streaming pipelines (since 2021)
- Introducing a Docker-based release train for OTA updates with DevSecOps and CI/CD (since 2018)
Utku U.
Last position:
Combining Neural Fields with Hypernetworks
- Developed a meta-learning approach with a teammate to merge multiple neural fields into a single scene representation using a hypernetwork.
- Implemented and evaluated the method on 2D (MNIST) and 3D (ShapeNet) data, showing faster inference compared to overfitting-based baselines.
Discover over 15,000 top freelancers
Statistics of experts using LSTM
Aggregated from the professional profiles of matched freelancers.
Experience
15 years
Position duration
1.9 years
Positions per freelancer
11
Top business areas
Information Technology, Business Intelligence, Product Development
Top industries
Information Technology, Manufacturing, Education
Certification focus areas
Business Intelligence, Information Technology, Marketing
Bachelor's degree or higher
100%
Master's degree or higher
100%
Doctorate
29%
Certifications per freelancer
3
Most common languages
German, English, Spanish
Speak two or more languages
100%
Based on our profile pool as of 29 Oct 2026.
Daily rate distribution
The chart shows how the daily rates of experts in this technology in Munich 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 in Munich using LSTM
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 29 Oct 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
LSTM 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 (100%)
- Manufacturing (71%)
- Education (57%)
- Banking and Finance (57%)
- Automotive (43%)
- Healthcare (43%)
- Retail (43%)
- Transportation (29%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Long Short-Term Memory Architectures
Long Short-Term Memory networks solve the vanishing gradient problem inherent in standard recurrent neural networks. By using input, forget, and output gating mechanisms, these deep learning architectures maintain long-range dependencies across ordered sequences. They excel in domains where the temporal order of observations determines the outcome.
Core Engineering Capabilities
- Multivariate time-series forecasting for sensor signals and telemetry
- Anomaly detection in industrial operations and continuous log streams
- Natural language sequence classification and text token processing
- Remaining useful life prediction for heavy industrial machinery
Tooling and Implementation Ecosystem
Implementation relies on major machine learning frameworks such as PyTorch, TensorFlow, and Keras. Sequence preprocessing pipelines depend on NumPy, pandas, and scikit-learn. Production serving utilizes ONNX Runtime, TensorRT, or TorchScript, alongside containerization via Docker and model registries managed in MLflow.
Sequential AI in the Munich Market
Organizations in Munich integrate sequence models into automotive telemetry, avionics, industrial IoT, and financial risk platforms. Bavarian engineering teams frequently deploy bidirectional variants and encoder-decoder setups on edge devices and embedded vehicle computers, requiring clean execution within strictly regulated German data environments.
Signals Demanding Specialist Intervention
- Traditional statistical models fail to capture non-linear temporal trends
- Standard RNNs experience exploding or vanishing gradients during training
- High-frequency IoT streams produce false positives in anomaly detection
- Transformer models exceed strict inference latency budgets on edge hardware
Hallmarks of Seasoned Sequence Specialists
Top practitioners design compact gating topologies that prevent overfitting on limited historical data. They select appropriate windowing strategies, manage stateful versus stateless hidden layers, and benchmark recurrent architectures against modern temporal convolutional alternatives to ensure the leanest model reaches production.
Frequently asked questions
Questions about LSTM? Start with the answers below.
An LSTM model processes continuous sequence data by capturing context across long intervals without losing earlier signals. Organizations apply this architecture to time-series forecasting, predictive maintenance, speech recognition, and early anomaly detection in complex hardware systems.
While Transformers excel at massive text corpora, Long Short-Term Memory networks run with substantially lower compute footprints on continuous, univariate or low-dimensional sensor data. They deliver predictable execution profiles on constrained edge devices where attention mechanisms introduce excessive computational overhead.
Specialists build recurrent pipelines primarily in PyTorch or TensorFlow alongside Keras for quick prototyping. Production deployment often utilizes ONNX Runtime to compress neural network execution layers for high-throughput operational systems.
A qualified LSTM professional combines deep linear algebra and calculus fundamentals with advanced data engineering in pandas and NumPy. They understand signal processing, Fourier transforms, cross-validation for chronological data, and model quantization techniques for rapid inference.
Yes, engineers routinely compile trained Long Short-Term Memory graphs to run on embedded microcontrollers and onboard vehicle units. Optimization libraries like TensorRT optimize gate calculations to meet microsecond latency constraints in automated manufacturing environments.
Review how the candidate addresses data leakage during chronological train-test splits and handles non-stationary baseline shifts. An experienced LSTM practitioner provides clear residual analyses, tests against baseline persistence models, and documents training stability across disparate seeds.
Companies in Munich frequently establish hybrid arrangements, enabling remote development while conducting data security validations and factory floor integrations on-site. Because southern German automotive and industrial hubs value tight hardware synergy, intermittent local presence ensures seamless field validation.
Technical collaboration, codebase documentation, and ML pipelines are handled in English across modern Munich tech hubs. However, working proficiency in German remains valuable when coordinating with plant engineers, compliance officers, and internal operational units.
The average hourly rate of freelancers in Munich, Germany who have used LSTM in their recent projects is 88 €, which corresponds to a daily rate of about 701 € based on an 8-hour working day.
Of the freelancers in Munich, Germany who have used LSTM in their recent projects, 100% hold at least a Bachelor's degree, 100% hold at least a Master's degree, and 29% hold a doctorate.
On average, freelancers in Munich, Germany who have used LSTM in their recent projects have 15 years of professional experience, with a single engagement typically lasting around 1.9 years.
The most common languages among freelancers in Munich, Germany who have used LSTM in their recent projects are German (100%), English (100%), and Spanish (29%).
The most common industries among freelancers in Munich, Germany who have used LSTM in their recent projects are Information Technology (100%), Manufacturing (71%), and Education (57%).
The most common business areas among freelancers in Munich, Germany who have used LSTM in their recent projects are Information Technology (100%), Business Intelligence (86%), and Product Development (86%).
Main locations of FRATCH Experts, who have recently used LSTM
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.
Countries:
Berlin Hamburg 
Munich Cologne Frankfurt Stuttgart Dusseldorf Leipzig Dortmund Essen Bremen Dresden Hanover Nuremberg Request a free demo
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Philipp Thomaschewski
FRATCH CEO
