LSTM Experts in Germany
for reliable sequence models, matched with vetted freelancers in minutesHire experts who design, train and deploy LSTM models for forecasting, language processing, anomaly detection and time-series analysis. FRATCH uses precise AI matching to connect you with vetted, available freelancers quickly.
Meet FRATCH Experts in Germany, who have recently used LSTM
Ashwin P.
Last position:
Freelance Data Scientist at Mercor Intelligence
- Architected and deployed end-to-end machine learning pipelines across classification and prediction datasets, ensuring robustness and reproducibility through MLOps best practices.
- Contributed directly to LLM model output accuracy improvement by designing and engineering specialised prompts grounded in end-to-end ML and SciML pipeline logic.
- Developed training data for large language models by formulating coding problems that models could not resolve and subsequently documenting the correct solutions.
Hakan A.
Last position:
Senior Software Engineer — AI Evaluation & Benchmarks at Diversido
- Provided technical leadership for a 4-engineer team delivering 3 major client platforms in 12 months with microservices architecture and scalability solutions — 100% of scoped majors shipped ahead of schedule vs. planned milestones (baseline: prior releases often slipped 1–2 sprints).
- Ran AI model evaluation and model outputs evaluation on LLM/AI vendor APIs: safety, completeness, instruction adherence, and groundedness review before go-live; cut escaped bad outputs in AI-integrated release checklists from recurring UAT findings to near-zero on final promote.
- Drove API development and performance optimization for payment, exchange, and AI services; fail-closed error handling and payload validation reduced integration rework cycles by ~35% vs. the first AI integration pass.
- Applied software testing, testing frameworks, code quality assurance, and code refactoring with continuous integration gates; first-pass PR acceptance improved across the team and production hotfixes on AI adapters dropped noticeably after review standards landed.
- Owned DevOps practices: Docker, GitHub Actions, Jenkins-compatible pipelines, and version control workflows — cut deployment time ~50% vs. pre-automation baseline and stabilized releases across 3 client environments.
- Implemented verifier/oracle-style pass-fail checks in container sandboxes (Harbor/Terminal-Bench aligned); wrote technical documentation so failures cleared in one review cycle.
- Led cross-functional collaboration with product and client stakeholders; translated AI evaluation scores and risk findings into plain-language briefs for non-technical partners, unblocking go/no-go decisions without extra engineering meetings.
- Used agile methodologies for sprint planning and backlog ownership; mentored engineers so mid-level contributors owned AI adapter modules independently by mid-engagement.
Heena P.
Last position:
Retirement Spend & Tax Optimizer Agentic AI App (Vibe Coding) at Personal Project
Self-directed exploration of agentic AI development methods, taken from idea to a working, publicly usable application
- Built an interactive planning tool for modelling retirement withdrawals and tax strategy using an agentic AI (vibe coding) development approach – demonstrating self-directed investigation of new AI-assisted development methods
- Delivered live, tax-aware spending projections and adjustable user inputs; shipped as a free, install-free browser application built in Python, with attention to usability for non-technical users
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.
Raphael M.
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
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.
Kiran Kumar K.
Last position:
Applied NLP: Word-Level Encoding for Smarter Event Predictions at University of Siegen
- Engineered 12+ Seq2Seq models (LSTMs/GRUs) to train an AI model, supporting AI Agent Evaluation Analyst and online projects in complex systems.
- Conducted 15+ experiments to improve forecasting accuracy by 28%, applying analytical thinking and testing models for QA and edge case coverage.
- Researched 20+ papers as a consultant to guide Large Language Model design, ensuring logical implications and domain of expertise alignment.
- Saved 40% compute time via model compression with reusable PyTorch framework, aiding developers and writers to suggest refinements and improve policies.
Mohammad Z.
Last position:
Scientist & Project Manager (Data-Driven Climate Analytics) at Leibniz Institute for Agricultural Engineering (ATB)
- Developed AI-powered analytics tools for drought prediction using CMIP6 data.
- Integrated UAV and satellite imagery to optimize predictive models for drought resilience.
- Designed a Decision Support System (DSS) for integrated water resources management.
- Partnered with industry and research institutions to deliver environmental monitoring solutions.
- Managed international projects, aligning data science innovations with policy and business goals.
Prajwal A.
Last position:
Master Thesis at Smart City Research Lab
From Crude to Crafted: Refining Participatory Design Data into Stakeholder-Ready Outcomes
- Architected a production Document AI platform using Retrieval Augmented Generation (RAG) over 1,500+ participatory design artefacts to answer historical project queries with grounded responses.
- Designed LLM evaluation combining RAGAS, custom evaluation metrics and human-in-the-loop (HITL) validation workflows to evaluate factual grounding, response quality, and prompt performance.
- Built a React, TypeScript, and D3.js frontend for interactive exploration of AI-generated insights.
- Implemented input layer LLM safety controls and Guardrails, including PII redaction and foul language filtering.
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).
Aravind S.
Last position:
AI – Data Specialist at Emirates Islamic Bank
- Architected and deployed LLM based AI agents, RAG pipelines, and vector search solutions for decision support across retail banking department.
- Developed and shipped robust AI pipelines with guardrails, error handling, monitoring, and fallback logic ensuring high reliability outcomes and compliance with data privacy.
- Developed and deployed ML models to identify transactional anomalies, improving fraud detection and risk assessment in high-volume datasets for credit risk modelling.
- Built, evaluated and fine-tuned ML models to generate propensity scores for customers used to drive personalized targeting campaigns for credit cards and personal finance/loan products.
- Developed an NLP pipeline using BERT embeddings and spaCy NER for SMS/email analysis and customer query logs.
- Trained machine learning models using Isolation Forest to classify user behaviour and detect anomalies.
- Extracted, cleaned, enriched and feature engineered datasets from different sources to build feature stores that powered ML model training.
- Led development of dashboards using Power BI, Grafana, and Prometheus to monitor model performances, KPI trends, and marketing metrics.
- Built multi-touch attribution models using logistic regression and time-decay weights to evaluate lead quality.
- Developed scalable ETL pipelines from CRM, T24, SAP, and ERP, supporting millions of monthly transactions.
- Integrated testing and CI/CD workflows for robust data pipeline deployment.
Mohammad L.
Last position:
Research Intern - ML / ADAS at IAV GmbH
- Developed and optimized LSTM-RNN and Decoder Transformer models to predict vehicle trajectory during target loss events in Adaptive Cruise Control systems, achieving 20% improved predictive accuracy over baseline models.
- Engineered novel data preprocessing pipeline from real road campaign data, processing multi-sensor time series data, generating 300+ training snippets.
- Implemented Bayesian hyperparameter optimization and applied physical constraints to prevent model run-away behavior, resulting in 30% smoother acceleration profiles.
- Extended existing patented technology for AI-assisted ACC function improvements, building upon foundational work to enhance network performance.
- Tools: Python, TensorFlow, Keras, Optuna, CarMaker
Geraldine C.
Last position:
Solution Engineer (Data & ML Integration) at Amadeus Data Processing GmbH
- Designed ML-ready data integration workflows between on-premise systems and cloud platforms (Snowflake, AWS Redshift, Azure), enabling scalable feature engineering and model deployment
- Implemented automated ML pipeline deployment using Python, SQL, and CI/CD tools, reducing model deployment time by 60%
- Developed data transformation logic for master data synchronization across ERP and analytics systems, ensuring data quality for predictive models
- Collaborated with cross-functional teams to translate business requirements into mathematical specifications for ML solutions
Abhijith Sai T.
Last position:
AI and AWS Developer at FannieMae
- Architected end-to-end credit risk pipelines by orchestrating Airflow ETLs and training LSTMs/Transformers to predict default and prepayment speeds on MBS portfolios.
- Developed Deep Learning NLP solutions using BERT and LayoutLM for document processing, leveraging Transfer Learning and custom PyTorch loss functions to automate underwriting.
- Optimized R&D lifecycles through Bayesian tuning, Batch Normalization, and MLflow tracking to ensure robust model performance throughout volatile mortgage market cycles.
- Productionized scalable MLOps infrastructure via Docker and INT8 Quantization, deploying low-latency FastAPI microservices on AWS SageMaker with automated CI/CD pipelines.
- Ensured regulatory compliance by integrating SHAP/LIME for explainability and establishing real-time Data Drift monitoring to meet strict FHFA and Fair Lending standards.
Atefeh K.
Last position:
Freelance AI Trainer at Outlier
- Designing and optimizing prompts for AI and machine learning models to improve reasoning, problem-solving, and scientific accuracy.
- Evaluating model performance and providing structured feedback to enhance consistency, reliability, and interpretability.
- Applying data-driven insights to refine AI outputs for technical and scientific applications.
- Developing practical experience in Machine Learning, AI evaluation, and prompt engineering for scientific use cases.
Discover over 15,000 top freelancers
Statistics of experts using LSTM
Aggregated from the professional profiles of matched freelancers.
Experience
9 years
Position duration
1.6 years
Positions per freelancer
7
Top business areas
Information Technology, Research and Development, Product Development
Top industries
Information Technology, Education, Banking and Finance
Certification focus areas
Information Technology, Business Intelligence, Product Development
Bachelor's degree or higher
100%
Master's degree or higher
90%
Doctorate
17%
Certifications per freelancer
2
Most common languages
German, English, French
Speak two or more languages
100%
Based on our profile pool as of 19 Sep 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology 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 of experts in Germany 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 19 Sep 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 (78%)
- Education (59%)
- Banking and Finance (50%)
- Professional Services (44%)
- Automotive (38%)
- Energy (28%)
- Manufacturing (28%)
- Healthcare (25%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
What LSTM does
LSTM, short for Long Short-Term Memory, is a recurrent neural network architecture built for sequential data. Its gated memory helps retain relevant information and reduce the vanishing-gradient problems that affect basic recurrent networks. Companies use it to learn patterns across ordered observations.
Typical applications
LSTM models are useful when current predictions depend on earlier events, measurements or tokens.
- Demand, energy and financial forecasting
- Speech, text and sequence classification
- Predictive maintenance and anomaly detection
- Sensor, activity and process monitoring
Ecosystem and tooling
LSTM work commonly sits within Python-based machine learning workflows using PyTorch, TensorFlow or Keras. Strong specialists also work with NumPy, pandas, scikit-learn, notebooks, data pipelines and experiment tracking. Production delivery may involve REST services, containers, cloud platforms and model monitoring.
When companies need expertise
Freelance expertise is valuable when a team has sequential data but lacks the capacity to shape it into a dependable model. Specialists can assess data quality, define windows and features, select a suitable architecture, establish baselines and connect training results to an operational system. In Germany, remote collaboration is common, while domain workshops may benefit from on-site sessions and clear English or German communication.
What strong professionals deliver
A capable LSTM professional treats the model as part of a complete solution. They prevent leakage between training and evaluation data, handle missing and irregular observations, tune sequence length, compare against simple baselines and explain trade-offs. They also document preprocessing, reproducible training and deployment constraints so the result can be maintained after handover.
Choosing the right approach
LSTM is not automatically the best option for every sequence problem. A strong assessment compares it with gated recurrent units, temporal convolutional networks, classical statistical methods and transformer-based models. The right choice depends on sequence length, data volume, latency, interpretability, compute limits and whether the project needs forecasting, classification or generation.
Frequently asked questions
Key details about LSTM, drawn from the questions we get asked most.
LSTM is used to model ordered data where earlier observations influence later outcomes. Common applications include demand forecasting, predictive maintenance, anomaly detection, speech processing, text classification and sensor analysis.
Long Short-Term Memory networks use gated memory to retain information across sequences. GRUs are often simpler and faster, while transformers can capture long-range relationships effectively but may require more data and computing resources. The right comparison depends on latency, sequence length, data volume and interpretability.
A strong LSTM specialist should understand data preparation, Python, PyTorch or TensorFlow, model evaluation and deployment. Experience with time-series validation, feature engineering, APIs, containers and monitoring is also valuable when the model must run in production.
The required background depends on the task and its risk. A professional handling a prototype may need less production exposure than someone responsible for a forecasting service or safety-related anomaly system. Review comparable sequence-modeling work, evaluation methods and the person’s ability to explain limitations.
LSTM projects are usually suitable for remote collaboration when data access, environments and documentation are available. Regular video workshops can cover requirements and model reviews, while on-site sessions may help with domain knowledge, sensitive systems or German-language stakeholder communication.
Ask whether the recurrent neural network was compared with a meaningful baseline and tested using a time-aware validation strategy. Quality also depends on leakage prevention, realistic error measures, robustness to changing data, reproducible training and clear deployment documentation.
LSTM may be a poor fit when a simpler statistical method solves the problem, when very long-range dependencies dominate, or when training and inference constraints favor another architecture. A careful professional should test alternatives rather than assume that recurrent modeling is the best choice.
Before using Long Short-Term Memory, clarify the prediction target, forecast horizon, data frequency, missing-value behavior, leakage risks and success criteria. Also confirm access to historical data, the deployment environment, expected latency, ownership of the trained model and the stakeholders who will evaluate its output.
The average hourly rate of freelancers in Germany who have used LSTM in their recent projects is 72 €, which corresponds to a daily rate of about 575 € based on an 8-hour working day.
Of the freelancers in Germany who have used LSTM in their recent projects, 100% hold at least a Bachelor's degree, 90% hold at least a Master's degree, and 17% hold a doctorate.
On average, freelancers in Germany who have used LSTM in their recent projects have 9 years of professional experience, with a single engagement typically lasting around 1.6 years.
The most common languages among freelancers in Germany who have used LSTM in their recent projects are German (100%), English (100%), and French (16%).
The most common industries among freelancers in Germany who have used LSTM in their recent projects are Information Technology (78%), Education (59%), and Banking and Finance (50%).
The most common business areas among freelancers in Germany who have used LSTM in their recent projects are Information Technology (94%), Research and Development (84%), and Product Development (78%).
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.
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
