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IT and developmentBackend developer

Middle applied AI / LLM Engineer (Python)

Mad DevsMad DevsPublished October 19
Candidates from
Kyrgyzstan
Open until
November 18, 2026

from 2,300 to 2,800 USD

  • Work formatRemote
  • CompanyMad Devs

We're looking for an AI Engineer to own the AI layer of our products, from research and experimentation to production deployment. You'll have end-to-end ownership of AI initiatives, work on real-world GenAI challenges, and help shape the future of AI-powered products. What you will do:- Design and build LLM pipelines, AI agents, and RAG-based systems, including function and tool calling and structured outputs using JSON schemas. - Develop production-grade Python services using Django, Celery, and Redis. - Validate product hypotheses using real-world data and analytics. - Measure and optimize LLM quality, performance, and cost. - Collaborate closely with product managers and engineers to turn ideas into scalable solutions. Requirements:- Experience with NLP projects in production, 1+ year. - Strong experience building production LLM applications. - Hands-on expertise with RAG, embeddings, vector search, prompt engineering, and LLM evaluation. - Experience with LangGraph, LangChain, or similar frameworks. - Solid Python backend background, 2+ years (Django, PostgreSQL, async programming). - Strong SQL/Pandas skills and a data-driven mindset. - Language proficiency: English at B2–C1, Russian at C1. - Location: any, except Russia and Belarus. Additional experience we value:- Hands-on work with vector databases such as Qdrant or pgvector. - Fine-tuning, text classification, and embedding model development. - Working knowledge of Kubernetes, gRPC, or Go. Why join us:At Mad Devs, you will join a company that values creativity, strategic thinking, and continuous improvement. You will work on projects that reach millions of users worldwide, alongside a strong and supportive team. Here, you will grow professionally, take on meaningful challenges, and contribute to products that solve real problems at scale.