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I will build a rag system or ai agent with llm automation
Albanda M
Über diesen Service
I build reliable RAG systems, AI agents and LLM automation workflows for real products and business use cases.
I am a Senior Software Engineer and AI Product Engineer with 10+ years of experience across backend architecture, AI applications, APIs and production software.
What I can help with:
RAG pipelines
Document ingestion and chunking
Embeddings and vector databases
Semantic search and metadata filtering
Grounded answers with citations
AI agents and tool calling
OpenAI, Claude and Gemini
Structured JSON outputs
Agent memory and state
API integrations
Evaluation and regression testing
Error handling and fallbacks
Production deployment
I treat LLM output as data that needs validation rather than automatically reliable truth. That is the difference between a demo and a system your customers can rely on.
For complex enterprise workflows, large datasets or multi agent systems, please message me before ordering so I can scope it properly.
Lerne Albanda M kennen
Albanda M
Senior AI Product Engineer, SaaS, RAG and Automation
- AusPolen
- Mitglied seitAug. 2026
- ⌀ Antwortzeit1 Stunde
Sprachen
Türkisch, Aserbaidschanisch, Englisch, Russisch, Polnisch, Arabisch
I help startups and businesses build production-ready AI SaaS platforms, ERP/CRM systems, automation tools, dashboards, browser extensions and desktop apps. I’m a Senior Software Engineer and AI product builder with 10+ years of experience in backend architecture, full-stack development, cloud systems and LLM-powered products. I can help with AI MVPs, RAG, LLM integrations, Next.js, React, Node.js, Python, PostgreSQL, APIs, technical leadership and scalable software architecture.
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FAQ
Can you work with PDF, Word or other documents?
Yes. The exact ingestion approach depends on the file types and how the documents are structured.
Can the system provide citations?
Yes. Citation backed responses can be included wherever the source data supports them.
Which vector databases can you use?
The best option depends on the project. I can work with common vector databases and with PostgreSQL based vector search.
Can you build AI agents that call APIs?
Yes. I can implement tool calling workflows where an agent interacts with supported APIs or internal services.
Can you evaluate the RAG system?
Yes. I can create evaluation cases covering retrieval quality, grounding, answer correctness and regressions.
Can you integrate this into my existing SaaS?
Yes, after reviewing your current architecture and APIs.
