FAISS Experts in Berlin
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Meet FRATCH Experts in Berlin, who have recently used FAISS
Aruldass Arulanandu
Last position:
Web Module Lead at Mphasis Limited
- Led the end-to-end delivery of enterprise full-stack web applications by driving requirement analysis, solution design, frontend and backend development, database design, API integration, code reviews, team coordination, Agile execution, CI/CD deployments, production support, performance optimization, security implementation, and stakeholder collaboration to deliver scalable, high-quality software solutions.
Muzamal Ali
Last position:
Data Scientist / AI Consultant at HelmX
- Delivered AI and data science solutions, including LLM-based chatbots and data pipelines, improving operational efficiency.
- Collaborated on product features, achieving measurable impact and maintaining strong client relationships.
Hamza Khan
Last position:
Academic Research Contributor in Health Sector (Volunteer)
- Acted as technical consultant to optimize multi-layer ensemble models combining ResNet, CNN-BiGRU-Attention, and XGBoost.
- Guided implementation of a Logistic Regression meta-learner to solve class imbalance problems, achieving 92.86% accuracy and 0.9644 AUC on PTB-XL and Chapman-Shaoxing datasets.
Jeet Pattanaik
Last position:
Global SAP Program Manager at Aldi Sued
- Pioneered first enterprise AI-SAP integration at ALDI SÜD, deploying AI-driven automation within one of retail's largest SAP S/4HANA programs, eliminating 50% of manual pre-cycle validation time and establishing replicable automation framework across 11 countries
- Led end-to-end SAP project lifecycle management for implementations across SAP S/4HANA and Manhattan Systems, supporting 7,300+ ALDI SÜD locations globally across Europe and Australia
- Served as primary executive liaison to C-level stakeholders across 11 countries for strategic SAP transformation programs
- Orchestrated automation, performance, and volume testing for critical releases, maintaining 99.9% system SLA compliance during peak retail periods
- Managed cross-functional international teams of 15+ specialists, delivering projects 20% faster than industry benchmarks
- Standardized SAP processes across 11 countries as part of one of retail's largest SAP implementations
- Directly managed €2M budget with 98% allocation accuracy across 12 concurrent projects
- Reduced SAP S/4HANA migration costs by 18% through strategic vendor contract renegotiations and optimization
Meisam Ghafarlangroudi
Last position:
Senior AI Engineer / Data Scientist at Geeks Ltd (WordUp)
Geeks Ltd is a UK-based technology company; WordUp is its AI-driven language-learning product focused on personalized vocabulary learning and intelligent educational experiences.
- Coordinate AI product delivery across Product, Engineering, Data, Operations, and leadership, translating user needs into scoped initiatives, sequencing work, surfacing blockers, facilitating hand-offs, and communicating progress.
- Own search, recommendation, retrieval, and content-enrichment features end to end, from requirements and architecture through Python/FastAPI implementation, testing, deployment, monitoring, and rapid iteration.
- Developed low-latency retrieval, ranking, and personalization services using AWS, OpenSearch, DynamoDB, embeddings, and reusable APIs, achieving <1s latency, 22% higher engagement, and 12% higher premium conversion.
- Use AI coding assistants for codebase analysis, scaffolding, refactoring, tests, debugging, and documentation while reviewing every output for correctness, architectural fit, security, maintainability, and user value.
- Represent technical work in planning and stakeholder discussions, gather requirements first-hand, challenge priorities constructively, explain delivery trade-offs, and help teammates make outcome-focused decisions.
Apoorv Singh
Last position:
AI Interviewer
- Built an AI Research Assistant with RAG, LangChain, LangGraph, and OpenAI LLMs integrated with vector search.
Kashaf Khan
Last position:
AI Consultant / Expert at Siemens Mobility
- Evaluated 45+ AI use cases and developed a prioritization framework for Siemens’ internal AI roadmap.
- Tested internal tools like DRIM, SiemensGPT, Microsoft Copilot; presented evaluation outcomes to stakeholders.
- Identified performance gaps and improved tool adoption by 65%.
- Supported AI knowledge-sharing initiatives, led tool onboarding sessions, and improved team AI literacy.
- Collaborated with engineering, procurement, and digital teams on tool feedback and strategy alignment.
Discover over 15,000 top freelancers
Statistics of experts using FAISS
Aggregated from the professional profiles of matched freelancers.
Experience
8 years
Position duration
2.1 years
Positions per freelancer
5
Top business areas
Information Technology, Product Development, Research and Development
Top industries
Information Technology, Banking and Finance, Healthcare
Certification focus areas
Information Technology, Product Development, Business Intelligence
Bachelor's degree or higher
100%
Master's degree or higher
43%
Certifications per freelancer
2
Most common languages
English, German, Persian
Speak two or more languages
71%
Based on our profile pool as of 30 Aug 2026.
Daily rate distribution
The chart shows how the daily rates of freelancers in this technology in Berlin 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 Berlin using FAISS
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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
Vector search
FAISS, short for Facebook AI Similarity Search, is used to find items that are close in vector space. Companies use it for semantic search, recommendation, duplicate detection, and retrieval in search-heavy products. It fits well when plain keyword search is not enough.
Core work
- Build ANN indexes for embedding search
- Tune recall and latency for production loads
- Choose index types for memory and speed
- Add filtering and ranking around vector results
Strong specialists understand data shape, distance metrics, and how vectors are generated upstream. They can explain trade-offs between accuracy, speed, and memory without hand-waving.
Ecosystem
FAISS often sits beside Python, PyTorch, NumPy, and embedding models from open-source or vendor stacks. In larger systems it connects to search APIs, databases, and rerankers. Experienced professionals know how to keep the retrieval layer simple, testable, and easy to operate.
When to bring in help
Companies usually look for freelance expertise when search quality drops, latency grows, or an index must move from prototype to production. This is common in Berlin teams building product search, media discovery, or internal knowledge lookup. Remote work is often enough, but on-site collaboration can help when data access or cross-team design is sensitive.
What good specialists do
A strong FAISS professional profiles data, picks the right index strategy, and validates results with real queries. They also watch memory use, batch updates, and deployment details. Clear documentation and practical handover matter, because FAISS is rarely the whole system.
Common project outcomes
- Semantic search for text, images, or audio embeddings
- Candidate retrieval before a reranking step
- Near-duplicate detection and clustering
- Retrieval for assistants and internal knowledge tools
Good work leaves the team with a stable retrieval layer, clear metrics, and a path for future index changes. That matters as much as raw speed.
Frequently asked questions
Everything clients usually want to know about FAISS, in one place.
FAISS is used for fast similarity search over vectors, often created from text, images, audio, or product data. Teams use it for semantic search, recommendations, duplicate detection, and retrieval before reranking. It is a good fit when exact keyword matching is too narrow.
FAISS is a library focused on nearest-neighbor search, while Elasticsearch and database vector features are broader search systems. FAISS is often chosen when the vector layer needs tight control over index design, memory use, and latency. Many teams still pair it with a search engine or database for filters, metadata, and application logic.
Yes. Facebook AI Similarity Search is the full name behind FAISS, and both names refer to the same vector search library. In practice, most people say FAISS when they talk about building or tuning similarity search.
A strong FAISS specialist usually knows embeddings, distance metrics, Python, and performance tuning. Knowledge of PyTorch, NumPy, retrieval workflows, and basic search ranking is also useful. If the project is production-facing, experience with APIs, monitoring, and deployment helps a lot.
For a prototype, a specialist who knows FAISS well can often get results quickly. For production, you want someone who has handled index updates, recall testing, and latency trade-offs under real traffic. The more data and query diversity you have, the more valuable that experience becomes.
Bring in FAISS expertise when you have embeddings but no reliable retrieval layer, or when the first index is too slow, too large, or not accurate enough. It also helps during migration from a proof of concept to a system that other teams depend on. Freelancers are useful when the need is focused and time-sensitive.
Most FAISS work can be done remotely, because the core tasks are code, data review, and search tuning. On-site time in Berlin can help when the work depends on sensitive data, close product workshops, or fast decisions across teams. The right setup depends on access, process, and security constraints.
Look for someone who can explain why a specific FAISS index fits your data, not just how to run the library. Good specialists validate results with real queries, measure latency and memory, and document trade-offs clearly. Ask for examples of retrieval systems they improved and how they tested quality.
The average hourly rate of freelancers in Berlin, Germany who have used FAISS in their recent projects is 66 €, which corresponds to a daily rate of about 531 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used FAISS in their recent projects, 100% hold at least a Bachelor's degree and 43% hold at least a Master's degree.
On average, freelancers in Berlin, Germany who have used FAISS in their recent projects have 8 years of professional experience, with a single engagement typically lasting around 2.1 years.
The most common languages among freelancers in Berlin, Germany who have used FAISS in their recent projects are English (100%), German (71%), and Persian (14%).
The most common industries among freelancers in Berlin, Germany who have used FAISS in their recent projects are Information Technology (100%), Banking and Finance (57%), and Healthcare (57%).
The most common business areas among freelancers in Berlin, Germany who have used FAISS in their recent projects are Information Technology (100%), Product Development (100%), and Research and Development (71%).
Main locations of FRATCH Experts, who have recently used FAISS
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
