Collaborative Filtering Experts in Germany
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Meet FRATCH Experts in Germany, who have recently used Collaborative Filtering
Mirza K.
Last position:
Agentic Automation and a RAG system
- This project involved extraction of intelligence data to support report writing for a company that provides geopolitical, global, commercial intelligence. The data have been gathered from a number of resources (interview transcripts, online data, internal documents), and then a knowledge base has been build from it. This was the basis of a complex RAG system, that was evaluated against a golden dataset. Agents have been used to find out the contradicting intelligence, the statements supporting each other, and to store back the generated knowledge.
Used: Python, RAG, LangGraph, LangChain, deepeval, MCP
Philipp G.
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
Stanley A.
Last position:
Senior AI Engineer & Technical Lead at Independent / Freelance
- TrendReel, production LLM agent and RAG system (Python, LangChain, OpenAI, Groq/Llama 3, Claude, FastAPI, Kubernetes, PostgreSQL).
- Designed and built a production multi-step LLM agent system: a script generation agent with a per-platform psychology database, 7 viral narrative frameworks, and structured quality scoring, switching between Claude and Groq backends in real time based on output metrics.
- Implemented multi-provider LLM routing (Claude primary, Groq/Llama 3 fallback) with priority-chain failover and quality-based provider switching, achieving 95% inference cost reduction while holding measurable quality thresholds.
- Built an advanced RAG-style retrieval pipeline with per-platform knowledge bases, semantic content matching, and structured output evaluation across 7 decision frameworks, directly analogous to multi-tenant context-based reasoning for enterprise document workflows.
- BrainyAI, adaptive AI learning platform (Python, LangChain, Groq Llama 3.3-70B, OpenAI, Next.js, Supabase, Redis).
- Integrated Groq Llama 3.3-70B with education-level-aware prompting, dynamically adjusting vocabulary depth, citation complexity, and reasoning style across four student proficiency tiers.
- Nexus Prime, multi-tenant SaaS platform for marketing and growth automation (25 modules, 99 backend routers, 153 frontend files).
- Built a 25-module, 99-router multi-tenant SaaS platform covering ad remix, affiliates, WhatsApp inbox, email, and cart recovery, serving four subscription tiers from $199 to $1,999 per month with integrated Stripe, Paystack, and Flutterwave billing.
- AI Video Surveillance Platform, multi-tenant edge and cloud computer vision system currently in active client pitch.
- Designed a multi-tenant AI video surveillance platform combining edge YOLO26 inference on NVIDIA Jetson Orin NX boxes with a central GKE cloud layer (Postgres, Pub/Sub, ClickHouse, R2, Keycloak) for event storage, dashboards, alerting, and multi-tenancy.
Danny-Michael B.
Last position:
Senior AI Engineer at Just Add AI GmbH
- Automatic detection of content on various documents
- Recommendation Engine
- Dynamic Pricing
Wolfram K.
Last position:
AI / Machine Learning Engineer (Projects & Applied AI) at UNIVERSITÉ PARIS 1 PANTHEON-SORBONNE & LIORA
- Designed and implemented a hybrid recommendation system (content-based + collaborative filtering)
- Built end-to-end ML pipelines including data processing, feature engineering, model training, and evaluation
- Developed RAG-based LLM systems using LangChain and vector databases for semantic search and knowledge retrieval
- Established MLOps workflows with MLflow for experiment tracking, versioning, and deployment readiness
- Implemented deep learning models (computer vision & classification) using PyTorch and TensorFlow
Steffen S.
Last position:
Senior Technical PM, CRM Core Experience & AI at Propstack GmbH (Scout24 S.E.)
- Built a JTBD-based prioritization framework for 3,000+ accumulated feature requests, identified 27 broker jobs, validated 8 through 25 user interviews, and used the resulting job map as a live prioritization filter for all incoming channels (Upvoty, CSAT, consulting tickets).
- Responsible for the Scout24 Lighthouse initiative: Document Intelligence with full RAG architecture (semantic chunking, bge-m3 embeddings, pgvector, BM25+Dense hybrid retrieval).
- Reduced lead time of customer feature requests to 3.1 days through code analysis, ticket specification, and independent implementation using a coding agent (Codex).
- Developed an LLM-based support agent (GPT-4o mini, Codex-generated merge requests) that reduced 3rd-level escalations from 40% to 5% of all monthly tickets.
- Integrated six partners through technical coordination, specification, backlog and release management, and led seven full stack developers.
- Eliminated regulatory exposure for brokers in six weeks through risk analysis (BGH ruling on distance selling/GDPR), new audit features, and coordination with legal and data protection officers.
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.
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.
Discover over 15,000 top freelancers
Statistics of experts using Collaborative Filtering
Aggregated from the professional profiles of matched freelancers.
Experience
17 years
Position duration
2.3 years
Positions per freelancer
10
Top business areas
Information Technology, Product Development, Business Intelligence
Top industries
Information Technology, Banking and Finance, Media and Entertainment
Certification focus areas
Research and Development, Information Technology, Business Intelligence
Bachelor's degree or higher
88%
Master's degree or higher
38%
Doctorate
25%
Certifications per freelancer
3
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 Collaborative Filtering
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.
Collaborative Filtering 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%)
- Banking and Finance (75%)
- Media and Entertainment (75%)
- Professional Services (75%)
- Automotive (50%)
- Education (50%)
- Retail (50%)
- Healthcare (38%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Recommendation foundations
Collaborative Filtering predicts what a person may prefer by comparing behavior across users, items or both. It can power product suggestions, personalized content feeds, related-item modules and ranked search results. The method learns from signals such as purchases, ratings, views, saves and skips instead of relying only on item descriptions.
Model approaches
User-based methods find people with similar behavior, while item-based methods compare relationships between products or content. Matrix factorization represents users and items in a shared latent space, and neural approaches can model richer interactions. Strong implementations distinguish explicit feedback, such as ratings, from implicit feedback, such as clicks or watch time.
Data and tooling
Successful systems combine data preparation, feature design, model training, evaluation and serving. Specialists may work with Python, pandas, NumPy, scikit-learn, PyTorch or TensorFlow, alongside SQL and distributed data tools. Common deliverables include:
- Interaction schemas and event pipelines
- Candidate-generation and ranking services
- Offline evaluation and experiment frameworks
- Batch or real-time recommendation APIs
Where companies use it
Collaborative Filtering fits marketplaces, online retail, streaming services, publishing, travel products and community platforms. It helps surface relevant choices when catalogs are broad and individual preferences are difficult to describe. In Germany, specialists may support both local product teams and international systems, working remotely or alongside data and product groups on site.
When freelance expertise helps
Companies often bring in a specialist when recommendations remain generic, new users receive weak results or model quality cannot be measured reliably. External support is also useful during a platform migration, a first recommendation launch or a move from batch suggestions to low-latency serving. Warning signs include sparse event data, unclear business goals and no feedback loop after release.
What strong specialists deliver
Effective professionals connect modeling decisions to user value and operational limits. They handle cold-start cases, popularity bias, changing catalogs, privacy expectations and feedback loops rather than treating an offline score as the final answer. They define meaningful test sets, monitor drift and explain trade-offs between relevance, diversity, latency and business rules.
Frequently asked questions
Quick answers to the questions that come up most around Collaborative Filtering.
Collaborative Filtering is used to recommend products, films, articles, music, destinations or other items from patterns in user behavior. It can identify related items, personalize feeds and rank choices without requiring a complete description of every item.
Collaborative Filtering learns from interactions between users and items, while content-based systems rely on item attributes such as category, text or technical features. Collaborative methods can uncover unexpected connections, but content-based methods may perform better when interaction data is sparse or a new item has just been added.
A strong Collaborative Filtering specialist usually understands data pipelines, SQL, Python, experimentation and model serving. Experience with matrix factorization, neural recommendation models, ranking, cloud infrastructure and privacy-aware event tracking is valuable when the system must run in production.
The right level depends on the task rather than a fixed number of years. A focused prototype may need someone who can prepare interaction data and evaluate a baseline, while a production system calls for experience with scale, cold-start handling, monitoring, retraining and online experiments.
Collaborative Filtering projects can be delivered remotely when data access, documentation and review processes are well defined. For teams in Germany, language expectations and occasional on-site workshops should be agreed early, especially when product, data and compliance stakeholders are distributed.
The main challenges of Collaborative Filtering are cold-start users and items, sparse interactions, popularity bias and changing preferences. A specialist should explain how the design combines behavioral signals with rules or content features where pure collaborative signals are not sufficient.
Ask a Collaborative Filtering specialist to show how they define useful feedback, separate training from evaluation data and test recommendations against a clear baseline. Quality also depends on diversity, coverage, latency, explainability and business outcomes, not only on one offline metric.
Before taking on a Collaborative Filtering project, clarify the available event data, item catalog, privacy constraints, serving environment and success criteria. It is also important to agree on ownership of pipelines, model updates, monitoring and the process for handling user feedback after launch.
The average hourly rate of freelancers in Germany who have used Collaborative Filtering in their recent projects is 110 €, which corresponds to a daily rate of about 883 € based on an 8-hour working day.
Of the freelancers in Germany who have used Collaborative Filtering in their recent projects, 88% hold at least a Bachelor's degree, 38% hold at least a Master's degree, and 25% hold a doctorate.
On average, freelancers in Germany who have used Collaborative Filtering in their recent projects have 17 years of professional experience, with a single engagement typically lasting around 2.3 years.
The most common languages among freelancers in Germany who have used Collaborative Filtering in their recent projects are German (100%), English (100%), and French (25%).
The most common industries among freelancers in Germany who have used Collaborative Filtering in their recent projects are Information Technology (100%), Banking and Finance (75%), and Media and Entertainment (75%).
The most common business areas among freelancers in Germany who have used Collaborative Filtering in their recent projects are Information Technology (100%), Product Development (100%), and Business Intelligence (88%).
Main locations of FRATCH Experts, who have recently used Collaborative Filtering
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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FRATCH CEO
