
AI Engineer
- Kraków, małopolskie
- Stała
- Pełny etat
- Experience in Python
- Experience in AI application development, focusing on cloud-based AI model integration, deployment, and optimization.
- Proficiency in programming languages such as Python, Java, and experience with AI/ML frameworks like Spring AI, LangChain, LangGraph, LlamaIndex, Agno, Pydantic, TensorFlow etc.
- Hands-on experience with Azure Cloud Services for AI, including Azure OpenAI and Azure AI Search for developing and deploying AI applications at scale.
- Proficiency in advanced AI architecture patterns, including Retrieval-Augmented Generation (RAG), Agentic RAG, MCP, Functional Calling, A2A especially in an Azure environment.
- Experience in prompt engineering, context engineering, vector databases, embedding/chunking strategies, and real-time data integration.
- Experience in evaluating model output and optimization
- Design, develop, and deploy AI-powered applications with a focus on cloud-native solutions.
- Integrate and optimize AI models within scalable cloud environments, particularly using Azure services.
- Write clean, efficient, and maintainable code in Python and Java.
- Utilize AI/ML frameworks such as Spring AI, LangChain, LangGraph, LlamaIndex, Agno, Pydantic, and TensorFlow to build intelligent systems.
- Leverage Azure OpenAI and Azure AI Search to build and deploy AI solutions at scale.
- Implement and manage cloud resources to support AI workloads and ensure high availability.
- Apply architectural patterns like Retrieval-Augmented Generation (RAG), Agentic RAG, MCP, Functional Calling, and A2A in AI solution design.
- Ensure alignment with best practices for performance, scalability, and maintainability in Azure environments.
- Develop and refine prompts for optimal model performance.
- Engineer context-aware systems using vector databases, embedding strategies, and chunking techniques.
- Build pipelines for real-time data ingestion and processing to support dynamic AI applications.
- Evaluate AI model outputs for accuracy, relevance, and performance.
- Continuously optimize models based on feedback, metrics, and evolving business needs.
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