System Analyst

Andersen is a company working on a project developing a micro-frontend solution to streamline processes and improve user access to services. The customer is a global enterprise focused on digital solutions that enable seamless interaction between people, services, and technology, operating across multiple domains with a mission to drive large-scale digital transformation by building sustainable, secure, and agile IT ecosystems.

• Commercial experience as a System Analyst for 4+ years
• Experience in AI/LLM domain including chatbots, document processing, and copilots
• Experience with RAG architectures, embeddings, and vector databases
• Experience in prompt design and evaluation
• Understanding of data modeling fundamentals and data pipelines
• Experience with SQL for data validation and exploration
• Experience in designing microservice-based and widget-based solutions
• Experience with API integrations and event-driven architectures
• Understanding of cloud ecosystems: AWS, Google Cloud, Microsoft Azure
• Experience in requirements elicitation including workshops and interviews
• Ability to decompose ambiguous problems into structured AI use cases
• Ability to assess feasibility of AI-driven solutions and estimate ROI and business value
• Experience in prioritizing AI use cases and defining product scope
• Experience in creating AI use case catalogs
• Ability to design High-Level Architecture (HLD)
• Experience in defining data requirements and creating data flow diagrams
• Experience in designing integration schemes (APIs and system interactions)
• Ability to define non-functional requirements (latency, security, scalability)
• Experience in defining MVP scope
• English language proficiency from Upper-Intermediate and above

Nice to have:

• Experience as a Business Analyst

• Analyzing functional capabilities of existing APIs
• Documenting user, functional, and non-functional requirements for frontend and REST API as user stories with acceptance criteria
• Modeling domain area (entity-relationship diagrams), business processes and algorithms (UML activity diagrams), functional capabilities (UML use case diagrams), and service interactions (UML sequence diagrams)
• Leading AI discovery phase with stakeholders
• Eliciting and structuring business and functional requirements
• Translating requirements into AI/ML solution design
• Defining end-to-end architecture (data, model, integration, UI/API)
• Identifying data sources, gaps, and data readiness
• Selecting between classic ML, LLM, or hybrid approaches (e.g., RAG)
• Defining success metrics (business and technical)
• Aligning solutions with IT architecture and constraints
• Preparing solution documentation including high-level design, diagrams, and flows

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