pgvector Experts in Berlin
in minutes from over 15,000 CVs with the power of AIHire experts who build vector search, semantic retrieval, and RAG-ready PostgreSQL setups with pgvector. Get specialists who tune embeddings, indexing, and query performance for production systems. Match fast with vetted, available freelancers.
Meet FRATCH Experts in Berlin, who have recently used pgvector
Abhishek Nair
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 Mishra
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
Jorge Nuricumbo
Last position:
Senior Developer at SafeXSmart KI Solutions UG
AI Platform Backend – Senior Developer
Brought in to design and build a backend for an AI platform from scratch, 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 single interface.
- Designed and developed a backend from scratch in C# / .NET 10, including domain modeling with DDD, a versioned RESTful API layer, and cloud infrastructure setup on Azure.
- Built a real-time chat infrastructure with Server-Sent Events (SSE), message persistence, and delivery guarantees for live operation of AI influencer personas at scale.
- Developed a media management service with integration of cloud object storage for upload and retrieval of 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
Sunish Bharathan
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.
Steffen Seitz
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.
Oleg Abrazhaev
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 Shcherban
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
Mathias Wilhelm
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
Meisam Ghafarlangroudi
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
16 years
Position duration
1.3 years
Positions per freelancer
11
Top business areas
Information Technology, Product Development, Project Management
Top industries
Information Technology, Automotive, Banking and Finance
Certification focus areas
Project Management, Business Intelligence, Information Technology
Bachelor's degree or higher
100%
Master's degree or higher
78%
Doctorate
11%
Certifications per freelancer
1
Most common languages
German, English, Ukrainian
Speak two or more languages
100%
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 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 30 Aug 2026. Actual rates may vary depending on seniority level, experience, skill specialization, project complexity, and engagement length.
About the technology
Vector search
pgvector adds vector similarity search to PostgreSQL. It is used for semantic search, recommendations, duplicate detection, and retrieval for LLM apps. Teams choose it when they want vector search without moving data out of Postgres.
Typical work
- Store and query embeddings in PostgreSQL
- Build semantic search and retrieval workflows
- Add similarity search to product catalogs and content systems
- Support RAG pipelines with Postgres data
- Tune indexes for faster nearest-neighbor queries
Core skills
Strong specialists know PostgreSQL, SQL, embeddings, distance metrics, and index design. They also understand how chunking, model choice, and query filters affect recall and latency. Good work is not only about adding the extension; it is about making search useful and stable.
Ecosystem
pgvector fits naturally into existing PostgreSQL stacks, from backend services to analytics and content platforms. It often appears with OpenAI, Sentence Transformers, LangChain, and other embedding pipelines. In Berlin, it is common in product teams that need search, AI features, or internal knowledge retrieval.
When to bring in help
Companies bring in freelance expertise when semantic search feels slow, results are poor, or the schema needs redesign. It also helps when a team wants to move from keyword search to vector search without replacing Postgres. A specialist can review indexes, filters, embedding storage, and query patterns quickly.
What good looks like
A strong pgvector professional ships clear query logic, sensible indexing, and clean data handling. They test similarity results against real use cases, not just toy data. They also explain trade-offs between recall, latency, and maintainability in plain language.
Frequently asked questions
What clients ask us most about pgvector — answered in short.
pgvector is used to add vector similarity search to PostgreSQL. Companies use it for semantic search, recommendations, duplicate detection, and retrieval for LLM workflows. It is a practical choice when the data already lives in Postgres and the team wants to keep it there.
pgvector keeps vector search inside PostgreSQL, which simplifies storage, joins, backups, and access control. A separate vector database can make sense when search volume or feature needs go beyond what Postgres should handle. For many teams, the main decision is whether they want one system or a dedicated search layer.
A strong pgvector specialist should understand embeddings, distance metrics, indexing, and how retrieval quality is evaluated. PostgreSQL tuning matters, but so do chunking strategy, metadata filters, and how the model creates vectors. Familiarity with RAG pipelines and application search flows is also valuable.
With pgvector, even a small proof of concept can work with basic PostgreSQL and vector concepts. Production work needs someone who can design the schema, choose the right index type, and measure search quality on real data. The more complex the filters, latency needs, and content volume, the more important deep experience becomes.
Yes. pgvector is often used to store embeddings for RAG systems, where retrieved context is pulled from PostgreSQL and sent to an LLM. The key is not only storing vectors, but also getting chunking, metadata, and ranking right.
Ask how the person would model embeddings, choose indexes, and test retrieval quality with your data. For pgvector, it also helps to ask how they handle updates, filters, and fallback search when vector results are weak. Clear answers usually show whether the specialist has shipped real systems.
Most pgvector work can be done remotely because the main tasks are schema design, query tuning, and application integration. On-site time only helps when the team wants close workshops with product, search, or data stakeholders. In Berlin, many companies mix remote delivery with a short local kickoff.
Look for someone who can explain the trade-offs behind their design, not just say they used pgvector. Good specialists show query examples, indexing choices, and how they validated search results with real inputs. They should also be comfortable discussing PostgreSQL behavior, performance limits, and how to keep the system maintainable.
The average hourly rate of freelancers in Berlin, Germany who have used pgvector in their recent projects is 82 €, which corresponds to a daily rate of about 659 € 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, 78% hold at least a Master's degree, and 11% hold a doctorate.
On average, freelancers in Berlin, Germany who have used pgvector in their recent projects have 16 years of professional experience, with a single engagement typically lasting around 1.3 years.
The most common languages among freelancers in Berlin, Germany who have used pgvector in their recent projects are German (100%), English (100%), and Ukrainian (22%).
The most common industries among freelancers in Berlin, Germany who have used pgvector in their recent projects are Information Technology (100%), Automotive (56%), and Banking and Finance (56%).
The most common business areas among freelancers in Berlin, Germany who have used pgvector in their recent projects are Information Technology (100%), Product Development (89%), and Project Management (89%).
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
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Philipp Thomaschewski
FRATCH CEO
