LSTM Experts in Germany
to build reliable sequence models with fast, precise AI matchingHire experts who design, train and deploy Long Short-Term Memory models for forecasting, speech processing, anomaly detection and text analysis. FRATCH matches you quickly with vetted, available freelancers whose skills fit your project requirements.
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.
Pradeep S.
Last position:
Tech Product Lead – AI, Data & Platform Products at Elli GmbH- A brand of Volkswagen
- Own the 12–18 month roadmap and key outcomes for Elli's enterprise customer platform, covering onboarding, pricing, billing, analytics and broader platform modernization; redesigned the Fleet onboarding funnel to double conversion, supporting a projected €20.7M revenue uplift by 2028.
- Lead the broader Energy Intelligence product and directly own its AI/ML, asset and portfolio-optimization capabilities, including MLOps and safe strategy deployment, strategy lifecycle management and backtesting; delegated data and V2G integration roadmap ownership to a new PO as the platform scope expanded.
- Built a Human-in-the-loop GenAI/RAG support workflow, increasing L1 resolution by 24%, routing accuracy to 91%, and reducing L2 workload by 30%.
- Introduced standardized data contracts and a self-service Python toolkit for traders and Data Scientists, increasing platform adoption by 15% and reducing support effort by 50%.
- Built and scaled a real-time orchestration product from 32 to 3,000+ endpoints across four markets, growing recurring revenue from €1.4k to €56.3k MRR.
- Developed product and AI capability across the organization, training 20 PMs on RAG, agents and prototyping; mentoring a junior PM and coaching an Enterprise Platform Tech Lead toward Product Management ownership.
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
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
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
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.
Pawan S.
Last position:
CAPTCHA Recognition using CRNN
- Built a CRNN model with VGG16 and BiLSTM backbone for text-based CAPTCHA recognition
- Achieved 9.37% character error rate and 68.36% sequence accuracy on validation data
- Expanded data augmentation pipeline with distortions, noise injection, and clutter to improve robustness
- Conducted detailed error analysis on confusable characters (O, Q, D) and proposed error-specific augmentation
- Tech Stack: Python, TensorFlow/Keras, OpenCV, NumPy, Matplotlib
Yosr S.
Last position:
Working Student – Software Development at TenneT
- Development of Python applications for data extraction and filtering using Tkinter and Bitbucket
- Digitization of a manual process, making workflows approximately 20% faster and more efficient
- Collaboration with specialist departments to implement process automation projects
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.
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.
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).
Tobias O.
Last position:
Python Developer at BundesMessenger
Implementation of new features in the Admin Backend
Automated tests (Unit, E2E)
Architecture coordination with external partners
Functionalities: user management, room administration, release automation.
Discover over 15,000 top freelancers
Statistics of experts using LSTM
Aggregated from the professional profiles of matched freelancers.
Experience
10 years
Position duration
1.6 years
Positions per freelancer
7
Top business areas
Information Technology, Product Development, Research and 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
91%
Doctorate
19%
Certifications per freelancer
2
Most common languages
German, English, French
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 Germany 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 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 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 (79%)
- Education (56%)
- Banking and Finance (50%)
- Professional Services (44%)
- Automotive (38%)
- Energy (29%)
- Manufacturing (26%)
- Healthcare (24%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Sequence Learning
Long Short-Term Memory, or LSTM, is a recurrent neural network architecture for learning patterns in ordered data. Its memory cells and gates help preserve useful context across long sequences while filtering out irrelevant signals. This makes LSTM suitable for time series, language, audio and event data where order matters.
Practical Applications
LSTM models appear in systems that must interpret changing information over time, including:
- Demand, energy and financial forecasting
- Speech recognition and audio classification
- Predictive maintenance and anomaly detection
- Text generation, classification and sequence labeling
- Sensor, clickstream and transaction analysis
The right design depends on sequence length, data quality, latency targets and the cost of incorrect predictions.
Ecosystem And Tooling
LSTM work commonly uses Python with PyTorch, TensorFlow or Keras, alongside NumPy, pandas and scikit-learn for preparation and evaluation. Strong specialists understand recurrent layers, embeddings, masking, batching, attention, regularization and checkpointing. They also connect experiments to data pipelines, model registries, APIs and monitoring services.
When To Bring In Expertise
Companies often need freelance expertise when historical data is available but a reliable forecasting or classification system is not. A specialist can assess whether an LSTM is appropriate, establish a baseline, prepare sequences and create a repeatable training workflow. In Germany, remote collaboration is common, while on-site work can help with industrial, automotive or production data that requires close contact with local teams.
Delivery And Integration
A useful LSTM project goes beyond a trained model. Professionals define target variables, prevent temporal leakage, select meaningful validation windows and document preprocessing. They package inference for batch or real-time use, connect it to existing services and set up monitoring for drift, latency and changing data patterns.
Signs Of Strong Specialists
Look for professionals who can explain why an LSTM is preferable to a simpler statistical model, a convolutional sequence model or a Transformer. They should show disciplined evaluation and clear trade-offs rather than relying on a single score. Good signals include:
- Careful handling of missing values, scaling and sequence boundaries
- Validation that respects time and production conditions
- Reproducible experiments and transparent documentation
- Practical attention to inference cost, maintenance and failure cases
Frequently asked questions
Key details about LSTM, drawn from the questions we get asked most.
Long Short-Term Memory (LSTM) is used to model ordered data where earlier events can influence later outcomes. Common applications include demand forecasting, predictive maintenance, speech processing, anomaly detection and text classification.
LSTM processes sequences step by step and can be effective when data volumes, latency or infrastructure are constrained. Transformers often handle long-range relationships and parallel training better, but they may require more data and computing resources.
LSTM work benefits from expertise in Python, PyTorch or TensorFlow, data preparation and time-series validation. A strong specialist should also understand deployment, APIs, monitoring, experiment tracking and the business meaning of the input data.
LSTM projects vary widely in difficulty, so the relevant experience is more important than a fixed duration. Match the specialist to the data type, production constraints and required deliverables, and ask for evidence of comparable forecasting, sequence or classification work.
LSTM projects are often suitable for remote collaboration when data access, documentation and communication processes are clear. On-site work may add value for industrial or operational use cases involving physical sensors, restricted systems or close coordination with German-speaking teams.
LSTM quality should be assessed with time-aware validation, meaningful business metrics and tests on realistic production data. The specialist should explain baseline comparisons, leakage controls, error patterns, inference performance and how the model will be monitored after release.
LSTM may be unsuitable when a simple statistical method already meets the need, when sequences are extremely long or when the project lacks enough representative training data. A responsible professional will compare alternatives such as gradient-boosted models, convolutional sequence models and Transformers.
Long Short-Term Memory engagements require clarity on the prediction target, forecast horizon, data ownership, evaluation method and deployment environment. Freelancers should also confirm access to historical data, labeling rules, latency expectations and who will maintain the model after handover.
The average hourly rate of freelancers in Germany who have used LSTM in their recent projects is 78 €, which corresponds to a daily rate of about 621 € 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, 91% hold at least a Master's degree, and 19% hold a doctorate.
On average, freelancers in Germany who have used LSTM in their recent projects have 10 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 (18%).
The most common industries among freelancers in Germany who have used LSTM in their recent projects are Information Technology (79%), Education (56%), 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%), Product Development (79%), and Research and Development (79%).
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 
Munich Request a free demo
Get in touch with the FRATCH team and we will get back to you within 4 hours.
Would you rather directly get in touch?
We always have the time for a call or email!
Philipp Thomaschewski
FRATCH CEO
