GenAI Functional Analyst
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In an Agile organization, you will be part of a multidisciplinary team (Marketeers, Analysts, Process Engineers, etc.), acting as the bridge between business and technical profiles within the Automation Squad on (Gen)AI topics.
As a Functional Analyst, you will analyze and translate requirements and processes into specifications. You will propose end-to-end solutions as part of the team, ensuring they meet business needs and are cost-efficient.
Through an iterative and adaptable approach, you will challenge requirements and specifications. By applying an MVP way of working, you will ensure rapid time to market and immediate value creation.
Experience
- Proven experience as an analyst on implementations of GenAI changes within a corporate organization.
- Proven experience with Generative AI technologies and applications such as Azure AI and AWS.
Technical Skills & Requirements
- AI Functional Knowledge: 3-5 years of experience.
- AWS: 3-5 years of experience.
- Trilingual EN/FR/NL: Fluency in English, French and Dutch (either French or Dutch can be at a lower level).
- Belgian Energy Sector: 3-5 years of experience.
- Python Scripting: 3-5 years of experience.
- Strong communication skills, essential for translating technical concepts related to GenAI to non-technical stakeholders.
- Ability to foster collaboration and build relationships across diverse teams.
- Demonstrated maturity in stakeholder management, with a focus on outcomes and value.
- Ability to challenge experienced AI engineers and analysts with a critical mindset.
- Knowledge of Generative AI principles, including how models such as Large Language Models (LLMs) work.
- Prompt Engineering and guardrail management skills.
- Basic or confirmed AWS AgentCore technical skills.
- Knowledge of Agentic AI within M365 and MS Copilot Studio.
- Familiarity with data analytics concepts and tools.
- Proficiency in programming languages such as Python, particularly for working with AI models and data manipulation.
- Ability to analyze, evaluate and visually represent (Gen)AI results.
- Knowledge of different measurements and KPIs related to (Gen)AI.
- Analysis and preparation of functional specifications.
- Automation and industrialization of activities.
- Analysis, evaluation and visualization of Generative AI results.
- Collaboration with business and technical stakeholders to define and implement end-to-end GenAI solutions.
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