Business Analyst GenAI

O projekcie: GenAI Business Analyst B2B | Hybrid from Kraków | We are looking for a GenAI Business Analyst with a strong business analysis background and hands-on exposure to Generative AI solutions. This role will focus on translating business needs into well-defined GenAI use cases and working closely with cross-functional teams to deliver AI-enabled products at scale. Wymagania: - Strong foundation in business analysis (requirements elicitation, process mapping, documentation, user journey design) - Proven ability to translate business objectives into epics, features, and user stories - Solid understanding of Generative AI and Large Language Models (LLMs) - Exposure to Retrieval-Augmented Generation (RAG) and understanding how GenAI systems ground responses - Experience working in API-driven architectures and understanding data flows across hybrid environments - Familiarity with prompt engineering and designing effective prompts and agent instructions - Understanding of function calling and API orchestration in agent-based workflows - Experience collaborating in cross-functional, agile teams Nice to Have: - Exposure to multi-agent systems, including agent personas, interactions, and collaboration patterns - Understanding of context and memory engineering in GenAI solutions - Knowledge of AI governance, including considerations for taking GenAI use cases to production - Familiarity with model monitoring approaches, metrics, and performance tracking - Experience with banking or financial services–related AI / GenAI use cases Codzienne zadania: - Elicit, analyse, and document business requirements using industry-standard techniques - Map business processes and design end-to-end user journeys for GenAI-enabled solutions - Translate business objectives into GenAI use cases, epics, features, and user stories - Collaborate closely with Product Managers, AI Engineers, Data Engineers, Architects, and UI/UX teams - Support the design and refinement of GenAI use cases, considering capabilities and limitations of LLMs - Contribute to the definition of prompts, agent roles, and instructions for effective AI interactions - Assist in designing agent workflows, including function calling and API orchestration - Support delivery of GenAI solutions within API-driven and hybrid (cloud and on-prem) ecosystems