AI Builder

Requirements

2+ years of experience working with AI/LLM-based applications or AI automation solutions.
Hands-on experience with LLM orchestration platforms such as Flowise, LangChain, LangGraph or similar tools.
Practical experience integrating and working with Claude API / Anthropic or other LLM APIs.
Strong understanding of Prompt Engineering, including prompt design, testing, evaluation and continuous optimization.
Experience designing and implementing RAG (Retrieval-Augmented Generation) solutions using internal knowledge bases.
Experience with Python development and scripting.
Hands-on experience building and consuming REST APIs, working with JSON and webhooks.
Experience integrating AI solutions with internal enterprise applications and external services.
Understanding of AI Agents, Agentic AI workflows, LLM orchestration and workflow automation.
Understanding of AI evaluation, testing, monitoring and reliability practices.
Ability to translate business requirements into practical AI-powered solutions.
Strong analytical and problem-solving skills.
Excellent communication and collaboration skills.
Curiosity and willingness to continuously learn and experiment with emerging AI technologies.
Experience working with document processing and knowledge management solutions is considered a plus.
Experience with DMS platforms or enterprise document management systems is considered a plus.
Familiarity with workflow orchestration and automation platforms is considered a plus.

Design, develop and maintain Agentic AI workflows using Claude API, Flowise and similar AI orchestration technologies.
Build AI-powered solutions that automate internal business and development workflows.
Implement integrations between AI workflows and internal enterprise platforms through REST/JSON APIs and webhooks.
Develop and maintain RAG solutions using internal knowledge bases and enterprise data sources.
Design prompts and continuously test, evaluate and optimize LLM responses and AI workflows.
Develop Python-based components and services supporting AI workflows and integrations.
Integrate AI solutions with internal platforms such as DMS Collibri, ticketing systems and other enterprise applications.
Work on AI-powered document processing, information extraction, knowledge retrieval and workflow automation use cases.
Identify opportunities for improving existing processes through AI automation.
Build prototypes and proof-of-concepts and evolve successful solutions into production-ready implementations.
Collaborate with technical and business stakeholders to understand requirements and translate them into AI solutions.
Monitor and improve the reliability, performance and quality of AI-powered workflows.
Contribute to the definition of reusable patterns and best practices for Agentic AI development.
Stay up to date with emerging LLM technologies, AI agents, orchestration frameworks and AI engineering practices.