Responsibilities:
Design, develop, and maintain LLM-powered solutions, including Retrieval-Augmented Generation (RAG) pipelines, entity extraction modules, and structured content generation frameworks
Implement and optimize LLM workflows using Azure OpenAI Service
Build scalable solutions for integrating vector databases, embeddings generation, document chunking, and metadata enrichment
Collaborate with data engineers on data pipelines and prompt chains in production environments
Define and implement prompt engineering strategies and fine-tuning approaches for LLM-based solutions
Ensure compliance with enterprise data governance, security, and regulatory standards, particularly in highly regulated industries (e.g., pharmaceuticals)
Required Skills & Qualifications:
Proven hands-on experience building LLM-based applications, including RAG pipelines and knowledge retrieval systems
Strong proficiency with Azure OpenAI (deployment, prompt design, rate limiting, model configuration)
Solid understanding of NLP techniques, including entity extraction, summarization, and classification
Experience designing document ingestion and embedding pipelines using tools such as LangChain or custom implementations
Familiarity with vector databases such as FAISS, Weaviate, or Pinecone
Practical knowledge of prompt engineering, few-shot learning techniques, and evaluation of generative outputs
Ability to write production-grade Python code and work in cloud-native environments (e.g., Databricks, Azure Functions)
Nice to have:
Experience with Databricks Model Serving, MLflow, and serving fine-tuned or custom LLMs via REST APIs
We offer:
B2B contract (up to 250 PLN/h net + VAT)
100% remote work
Wide range of projects (internal and international)
Dedicated certification budget
Annual evaluation meetings to define an individual development path
Benefits package
Integration trips
Net per hour - B2B
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