pgvector Experts in Berlin
matched in minutes by AIHire experts who design semantic search, recommendation and retrieval systems with PostgreSQL, embeddings and vector indexing. FRATCH connects you with vetted, available freelancers whose skills match your project precisely and quickly.
Meet FRATCH Experts in Berlin, who have recently used pgvector
Stefan O.
Last position:
Founder at ProtocolEngine.io
Evidence-led health intelligence platform turning published research into personal health protocols. It scores 430 habits, foods, and supplements against the studies behind them, and moves the score when the evidence moves. Built solo.
- Built the daily ingestion pipeline across PubMed, bioRxiv, and medRxiv: 43,000+ papers from 3,400+ journals processed into 230,000+ typed evidence claims, each one traceable back to the study it came from.
- Designed the six-factor evidence scoring model and the public changelog behind it, so no recommendation ever appears without the papers underneath it. 23,000+ grade changes recorded and explained to date.
- Shipped an entity information model connecting every intervention to its mechanisms, biomarkers, and outcomes: 118 biomarkers with region-specific reference ranges, 77 mechanisms, 32 graded outcomes.
- Built the personalisation layer: blood panel ingestion that reads lab PDFs with a vision model and corrects results for draw time against the user's wake anchor, plus Oura, WHOOP, and Withings integration for daily readiness context.
- Operate eleven specialised review agents over the corpus and codebase, covering paper curation, retrieval quality, health-claim compliance across EU and US regimes, and security.
- Shipped the Evidence Assistant, a RAG assistant that answers from the claim database and cites the underlying papers, plus a B2B practitioner tier, an Expo React Native app, and localisation across 3 languages and 7 markets.
Stack: Next.js 16, TypeScript, Supabase, pgvector, Anthropic Claude, Vercel, DeepInfra.
Oleg A.
Last position:
Staff Software Engineer at Kpler Germany GmbH
- Delivered a new notifications platform implementation built from scratch to replace existing and upcoming services
- Collaborating with other teams to integrate more domains
Tech stack:
- Data: Scala 3, Apache Kafka, Python, Airflow, Astronomer
- BE-FE: TypeScript, NestJS, Java, Spring Boot, Vue
- Dev-ops: AWS, PostgreSQL, Docker, GitHub Actions, Kubernetes, Helm, ArgoCD
Viktor S.
Last position:
AI Engineer (Freelance) at Empion
Enterprise AI content categorization and AI-powered web research.
- Built multi-LLM evaluation framework with annotated data
- Iterated LLM error rates based on annotated datasets
- Implemented AI-powered web research pipeline Stack: LLM, evals, OpenRouter, Python, Node.js, TypeScript, React
Jorge N.
Last position:
Senior Developer at NDA
AI Platform Backend – Senior Developer
Hired to design and build an AI platform backend from the ground up, including multi-provider LLM orchestration and real-time infrastructure for AI influencer personas at scale.
Tasks and Responsibilities
- Architected and implemented a multi-LLM orchestration layer with Semantic Kernel to integrate GPT-4 and other providers for core platform logic and AI influencer personas, reducing model-switching overhead by abstracting provider APIs behind a unified interface.
- Designed and developed a backend from the ground up in C# / .NET 10, including domain modeling with DDD, a versioned RESTful API layer, and cloud infrastructure on Azure.
- Built real-time chat infrastructure with Server-Sent Events (SSE), message persistence, and delivery guarantees for running AI influencer personas live at scale.
- Developed a media management service with cloud object storage integration for uploading and retrieving influencer-generated content.
- Created an integration and unit test suite with data seeding for reliable regression testing across all core platform flows, significantly reducing the production error rate.
Tools and Technologies: C#, .NET, ASP.NET Core, Python, TypeScript, MySQL, Semantic Kernel, EF Core, Minimal APIs, LLM Orchestration, Prompt Engineering, Agentic AI, Generative AI, AI-Assisted Engineering, Claude Code, GitHub Copilot, Google Gemini, OpenAI API, Ollama, Redis, Azure, Azure Container Apps, Azure Database for MySQL, Docker, GitHub Actions, Clean Architecture, Vertical Slice Architecture, CQRS, Domain-Driven Design, REST API, xUnit, Integration Testing, Unit Testing, Jira, Confluence, Scrum
Murad H.
Last position:
Founder & Technical Lead at Hubpoint.Ai
- Founded an AI-powered scheduling and business-management SaaS for SMBs, owning technology strategy, architecture, product development, UX, billing and go-to-market execution.
- Architected and shipped a multi-tenant platform with REST APIs, RBAC, CRM, billing and notifications, powering the manager dashboard, admin console, booking experience and iOS/Android applications.
- Led and mentored 7 software engineers, 1 DevOps engineer, 1 QA engineer and 1 UX/UI designer, while remaining hands-on across backend, frontend and product delivery.
- Built AI voice and chat agents using Python/FastAPI, OpenAI and Anthropic APIs, RAG, pgvector and tool calling; integrated Twilio, Google Calendar/Meet, Stripe and Firebase.
- Owned production infrastructure and automated delivery across separate environments using Docker, Nginx, GitHub Actions and Grafana; represented the company at accelerators and international startup events.
Selected stack: Python, FastAPI, Node.js, Vue 3, React/Next.js, React Native, PostgreSQL, Redis, Docker
Sunish B.
Last position:
AtlasMind - Production AI assistant for Jira at Mercedes Benz Innovation Labs Gmbh
- Converts natural language into JQL using RAG and pgvector. Returns structured JSON with a query, chart spec, and plain-text answer. A two-stage router answers general questions without touching the JQL pipeline at all.
- Interchangeable LLM backends: Ollama, vLLM, Groq, Anthropic Claude, AWS Bedrock - switchable at runtime, no code changes. Self-healing JQL: on Jira validation failure, feeds error back to LLM, retries up to 4 times. OCI Vault for secrets. Deployed on Oracle Cloud A1 with GPU inference over Tailscale private network. Open source.
Erik W.
Last position:
AI Workflow and Process Automation at Self-employed
- Process analysis and target concept: interviews with responsible stakeholders and users, system and handover model, bottleneck analysis and acceptance criteria.
- Clearly defined automation modules, built with AI support using Python/FastAPI, TypeScript/Next.js, SQL/PostgreSQL, Supabase, REST APIs and Webhooks; specification, acceptance criteria and acceptance testing handled by me.
- Document and decision workflows from intake, research and extraction through to decision documents, CRM updates or controlled system actions.
- Quality assurance and handover with test cases, logging, exception paths, operational documentation and knowledge transfer.
Mathias W.
Last position:
Implementation of an on-premise OCR solution with information extraction at Mindhopper GmbH
- Insurance service provider*
Challenge: Business-critical documents were processed through external OCR providers, with ongoing costs, dependency, and data privacy risks for sensitive insurance data.
Implementation:
- Architecture and production implementation of an on-premise OCR solution with full data ownership
- Methods for recognizing document structures as the basis for automated further processing
- ML-, NLP-, and LLM/VLM-based information extraction, especially from invoices and quotations
Success: Replaced external providers: full data ownership, GDPR-compliant processing, and 75% lower recurring OCR costs per year
Used technologies: Python, Docker, Microservices, FastAPI, PyTorch, Torchvision, MongoDB, MySQL
Abhishek N.
Last position:
Fullstack Developer at DAMALO GmbH
- Own full-stack development of an AI-native enterprise platform built on TypeScript, React, Vite, tRPC, Hono, and PostgreSQL, delivering AI-powered consulting workflows to B2B clients.
- Designed and shipped a multi-agent AI system using ReAct framework and Claude skills-style workflow patterns, including an intelligent PM assistant with rich system prompts, slash commands, tool integrations, and streaming chat UI.
- Architected an LLM evaluation framework: rubric-based LLM-as-judge, golden datasets, regression testing, and automated quality gating — ensuring consistent AI output quality at scale.
- Integrated LangFuse for end-to-end LLM tracing, conversation replays, and evaluation pipelines, enabling data-driven prompt optimisation that reduced token costs and response variance.
- Built with Drizzle ORM, pgvector, and knowledge graphs for structured data access, semantic search, and relationship-aware AI reasoning across the platform.
- Led TanStack React Query migration across the application — replacing manual state management with centralised caching and automatic refetching, reducing data-fetching boilerplate significantly.
- Practiced AI-native development throughout: Claude Code, Codex, Perplexity SDK, and LLM-assisted testing across the full development lifecycle. Deployed on Vercel + Azure ACA with Biome for linting/formatting.
Deepak M.
Last position:
Lead ML Platform Engineer at Billie GmbH
- Mentor team of 6 ML platform engineers through weekly 1:1s, technical design reviews, and best practices, improving team velocity by 35% through structured sprint planning and skill development programs
- Define 2025–2026 ML platform roadmap in collaboration with Data Science, Cloud Engineering, and Product teams, prioritizing automated model governance, cost attribution systems, and multi-environment deployment strategies
- Partner with Data Science, SRE, and Product stakeholders to align ML platform capabilities with business objectives, reducing data scientist deployment friction by 60% through self-service platforms
- Architect and deliver production-grade MLOps platform supporting 50+ models in production with automated promotion pipelines, versioning, and rollback capabilities, achieving 99.5% platform uptime SLA
- Design distributed ML pipeline architecture using Metaflow and Argo Workflows (Vertex Pipelines-compatible), reducing model training time by 30% and deployment cycles from 2 weeks to 3 days through full CI/CD automation
- Build containerized ML services on Kubernetes with auto-scaling policies, resource quotas, and multi-tenancy isolation, optimizing infrastructure costs by $180K annually (25% reduction)
- Implement monitoring, alerting, and performance tracking using Prometheus, Grafana, and custom instrumentation, reducing model debugging time by 50% and establishing model performance SLOs
- Lead development of RAG-based document intelligence platform using LangChain, LangGraph, and vector databases, implementing agentic AI workflows for automated financial document processing
- Implement Infrastructure-as-Code using Terraform for reproducible environment provisioning and GitOps workflows, reducing infrastructure drift incidents by 80%
- Design role-based access control for ML platform, implement model lineage tracking, and establish audit trails for regulatory compliance aligned with enterprise IAM best practices
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.
Meisam G.
Last position:
Machine Learning Engineer at Geeks
- Utilized a Large Language Model (LLM) at WordUp, tailored to enhance vocabulary learning by understanding and generating contextual examples, improving personalized learning experiences
- Developed a high-performance Fast API service for retrieving high-K similar vectors with batch querying capabilities. This service is crucial for enabling efficient Retrieval Augmented Generation (RAG) and semantic search applications
- Designed and implemented a high-performance Python ETL pipeline, optimizing CPU and I/O utilization and streamlining data cleansing logic, resulting in a 30% reduction in processing time
- Utilized machine learning to analyze user behavior and predict churn, identifying key engagement trends that led to a 15% increase in user retention and satisfaction
- Developed a Customer Lifetime Value (CLTV) prediction model, leading to a 10% increase in average CLTV through targeted retention efforts
Discover over 15,000 top freelancers
Statistics of experts using pgvector
Aggregated from the professional profiles of matched freelancers.
Experience
15 years
Position duration
1.5 years
Positions per freelancer
10
Top business areas
Information Technology, Product Development, Project Management
Top industries
Information Technology, Banking and Finance, Healthcare
Certification focus areas
Information Technology, Project Management, Product Development
Bachelor's degree or higher
100%
Master's degree or higher
83%
Doctorate
8%
Certifications per freelancer
2
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 Berlin 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 Berlin using pgvector
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.
pgvector 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 (58%)
- Healthcare (50%)
- Automotive (42%)
- Retail (42%)
- Education (33%)
- Media and Entertainment (33%)
- Transportation (25%)
Please note that freelancers can work across multiple industries, so percentages overlap.
About the technology
Vector search in PostgreSQL
pgvector is an open-source PostgreSQL extension for storing, indexing and searching vector embeddings alongside relational data. It supports similarity search for text, images and other machine-generated representations without forcing data into a separate vector database. Teams use it to add semantic retrieval to existing PostgreSQL applications.
Search and retrieval use cases
pgvector helps companies connect user intent with relevant records rather than relying only on exact keywords. Typical delivery work includes:
- Semantic search across product, support or knowledge content
- Retrieval-augmented generation pipelines for language applications
- Recommendation and similarity features for catalogues and media
- Duplicate detection, matching and entity resolution
Indexing and query design
Strong implementations combine PostgreSQL data modelling with embedding and query expertise. Specialists choose between exact searches and approximate indexes such as HNSW or IVFFlat, then tune distance operators, filtering, recall and latency for the workload. They also plan migrations, reindexing and operational safeguards.
Connected ecosystem
Projects commonly connect pgvector with embedding APIs, open-source models, Python or JavaScript services, and frameworks such as LangChain or LlamaIndex. Expertise in PostgreSQL transactions, SQL, extensions, data pipelines and application observability matters as much as vector concepts. Metadata filters and hybrid keyword-plus-vector search often complete the design.
When freelance expertise helps
Companies often bring in a specialist when a prototype must become a reliable product or when an existing search experience is not finding relevant results. In Berlin, local teams may need on-site workshops alongside remote delivery, with clear communication across product, data and software groups. A focused engagement can cover schema design, evaluation, integration and handover.
Signs of strong specialists
Look for professionals who explain trade-offs in measurable product terms and can inspect the whole retrieval path, not only the database extension. Useful evidence includes:
- A clear evaluation method using representative queries and labelled results
- Practical choices around HNSW, IVFFlat, filtering and index maintenance
- Secure handling of source data, embeddings and model-generated content
- Production experience with monitoring, migrations and failure recovery
Frequently asked questions
What clients ask us most about pgvector — answered in short.
pgvector adds vector storage and similarity search to PostgreSQL. Companies use it for semantic search, recommendations, document retrieval, duplicate detection and retrieval-augmented generation while keeping embeddings close to relational data.
pgvector is a strong choice when PostgreSQL already stores the application data and the team values SQL, transactions and one operational system. A dedicated vector database may suit very large or highly specialized workloads, so the decision depends on scale, filtering needs, latency targets and operational preferences.
A capable pgvector specialist should understand PostgreSQL schema design, SQL query planning, embedding models and data pipelines. Experience with Python or JavaScript services, hybrid search, LangChain or LlamaIndex, and production observability is also useful.
The right level depends on the work. A contained proof of concept may need strong PostgreSQL and embedding knowledge, while a production migration calls for experience with index selection, query evaluation, data security, monitoring and recovery procedures.
Yes. pgvector work is often suitable for remote collaboration because schema reviews, query testing and retrieval evaluation can be handled through shared repositories and environments. Berlin-based teams may still prefer on-site workshops for discovery, with German or English communication agreed at the start.
pgvector is useful when users express intent in different words or when records need to be matched by meaning, style or visual similarity. Keyword search remains valuable for exact names, identifiers and filters, so many strong systems combine both approaches.
Ask for a representative evaluation set, clear relevance criteria and evidence that index and query choices were tested against real product needs. A strong pgvector implementation also documents embedding changes, filter behaviour, monitoring and how results degrade when services or indexes are unavailable.
pgvector indexing requires trade-offs between search accuracy, response time, memory use and maintenance effort. HNSW and IVFFlat behave differently, while filters, changing embeddings, concurrent writes and PostgreSQL resource limits can all affect results and operational stability.
The average hourly rate of freelancers in Berlin, Germany who have used pgvector in their recent projects is 92 €, which corresponds to a daily rate of about 732 € based on an 8-hour working day.
Of the freelancers in Berlin, Germany who have used pgvector in their recent projects, 100% hold at least a Bachelor's degree, 83% hold at least a Master's degree, and 8% hold a doctorate.
On average, freelancers in Berlin, Germany who have used pgvector in their recent projects have 15 years of professional experience, with a single engagement typically lasting around 1.5 years.
The most common languages among freelancers in Berlin, Germany who have used pgvector in their recent projects are German (100%), English (100%), and Spanish (17%).
The most common industries among freelancers in Berlin, Germany who have used pgvector in their recent projects are Information Technology (100%), Banking and Finance (58%), and Healthcare (50%).
The most common business areas among freelancers in Berlin, Germany who have used pgvector in their recent projects are Information Technology (100%), Product Development (92%), and Project Management (83%).
Main locations of FRATCH Experts, who have recently used pgvector
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
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
