AI Solutions Developer

Analityka rynku

Stack

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Opis

Accountabilities - AI Application Development: Design, develop, and deploy custom AI agents, intelligent applications, and automated workflows using Python and frameworks such as LangChain, AutoGen, or CrewAI. - Backend Engineering: Build and maintain backend services, microservices, and supporting logic to ensure AI applications are scalable, reliable, and highly available. - RAG Systems and Knowledge Integration: Develop retrieval-augmented generation pipelines connecting LLMs to proprietary information using PostgreSQL with pgvector and vector databases such as Pinecone, Weaviate, and FAISS. - Full-Stack Development: Create end-to-end AI applications with intuitive user interfaces, robust backend functionality, and secure integrations for internal and external users. - Workflow Automation: Build self-service tools and intelligent workflows that automate repetitive tasks, streamline content creation, support localization, and improve training and sales enablement processes. - API Development and Integration: Design secure RESTful APIs and integrate AI solutions with business applications, learning platforms, sales enablement systems, knowledge repositories, and third-party services. - Conversational AI Capabilities: Implement conversation management, context retention, structured outputs, and tool calling to support interactive guidance and multistep workflows. Evaluate emerging capabilities in speech recognition, speech generation, interactive avatars, and multilingual app

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