LLM Application Engineer

1 292 264 Kč (CZK)/yr

621,28 Kč (CZK)/hr

55 826 Kč (CZK)/yr

The average llm application engineer gross salary in Czech Republic is 1 292 264 Kč or an equivalent hourly rate of 621 Kč. In addition, they earn an average bonus of 55 826 Kč. Salary estimates based on salary survey data collected directly from employers and anonymous employees in Czech Republic. An entry level llm application engineer (1-3 years of experience) earns an average salary of 907 896 Kč. On the other end, a senior level llm application engineer (8+ years of experience) earns an average salary of 1 460 529 Kč.

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Salary Potential

1 543 393 Kč (CZK)

19 %

Based on our compensation data, the estimated salary potential for LLM Application Engineer will increase 19 % over 5 years.

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Country Overview

Czech Republic

Cost of living is calculated based on accumulating the cost of food, transportation, health services, rent, utilities, taxes, and miscellaneous.

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The Czech Republic, also known as Czechia and historically known as Bohemia, is a landlocked country in Central Europe. The country is bordered by Austria to the south, Germany to the west, Poland to the northeast, and Slovakia to the southeast. The Czech Republic has a hilly landscape that covers an area of 78,871 square kilometers (30,452 sq mi) with a mostly temperate continental and oceanic climate. The capital and largest city is Prague; other major cities and urban areas include Brno, Ostr

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This page is an excerpt of the much more complete compensation information available in ERI's Assessor Series.

ERI's compensation data are based on salary surveys conducted and researched by ERI. Cost of labor data in the Assessor Series are based on actual housing sales data from commercially available sources, plus rental rates, gasoline prices, consumables, medical care premium costs, property taxes, effective income tax rates, etc.

Job Description
Designs, builds, and optimizes production applications powered by large language models (LLMs). Integrates LLMs with internal systems and external services to deliver reliable, safe, and cost-efficient AI capabilities across search, summarization, generation, and agentic workflows. Collaborates with Product, Data, and Platform teams to translate use cases into robust retrieval-augmented generation (RAG) pipelines, evaluation harnesses, and monitoring that meet latency, quality,..., and governance targets. Designs LLM application architectures, including orchestration layers, context construction, and tool/API integrations. Implements retrieval-augmented generation pipelines using embeddings, vector databases, and document chunking strategies tuned to domain content. Engineers prompts, guards, and system instructions; versions prompt assets and curates reusable prompt libraries for common tasks.Read More
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