AI Business Analyst

Job Title: AI Business Analyst

Experience: 4–8 Years

Employment Type: Full-Time

Location: Sydney/Melbourne

Job Role

We are looking for an AI Business Analyst who can work closely with customers and business stakeholders to identify business challenges, translate them into AI-driven use cases.

The role requires strong business analysis skills combined with solid conceptual knowledge of AI and Generative AI — enough to have credible, detailed conversations with technical teams and challenge what's realistic, without needing to build the solution personally. 

Roles & Responsibilities

• Engage with customers and business stakeholders to understand business processes, challenges, objectives, and requirements.
• Identify and evaluate opportunities for AI, Machine Learning, Generative AI, and automation to solve business problems.
• Translate business requirements into AI use cases, user stories, functional requirements, workflows, and acceptance criteria.
• Analyse existing business processes and identify where AI can improve productivity, accuracy, or customer experience.
• Partner with Data Scientists, ML Engineers, and Software Engineers to shape and refine AI solution design — asking the right questions, not writing the code.
• Act as the functional bridge between customers and internal engineering teams, ensuring requirements are understood correctly on both sides.
• Support POCs and pilots by defining success criteria, validating outputs against business intent, and gathering stakeholder feedback.
• Understand LLMs, RAG, AI Agents, prompt engineering, and embeddings well enough to assess whether a proposed solution will realistically meet the business need.
• Evaluate AI outputs for accuracy, relevance, and business suitability — flagging gaps for the technical team to address.
• Work with technical teams to understand APIs, data sources, and integration constraints as they affect requirements (not to build the integrations).
• Support data analysis and validation of business assumptions using SQL and other analytical tools.
• Support UAT by defining test scenarios and validating results against business requirements.
• Prepare and maintain BRDs, FRDs, user stories, process flows, and functional specifications.
• Track requirements, risks, issues, dependencies, and deliverables.
• Run solution demonstrations and communicate AI capabilities and outcomes to customers and stakeholders in plain business language.
• Stay current on emerging AI/GenAI trends and spot where they apply to real business problems.

Must-Have Skills

• Strong experience in Business Analysis, Technical Business Analysis, or Solution Consulting.
• Solid conceptual understanding of AI and Generative AI — what these systems can and can't reliably do.
• Working knowledge of LLMs, RAG, and AI Agent concepts (conceptual fluency, not hands-on model building).
• Understanding of prompt engineering and how LLM outputs are evaluated.
• Strong experience in requirements gathering, documentation, and stakeholder management.
• Ability to translate business requirements into functional requirements and AI use cases.
• Strong analytical and problem-solving skills.
• Working understanding of APIs, databases, and data flows — enough to assess feasibility and write accurate requirements.
• Strong communication and presentation skills, including explaining technical trade-offs to non-technical stakeholders.
• Experience working directly with customers/business stakeholders.
• Ability to work effectively with Data Scientists, ML Engineers, and Software Engineers as a peer, not a passenger.

Required Skills

• Generative AI / LLM concepts
• RAG and AI Agent concepts
• Business Analysis & Requirements Engineering
• SQL & Data Analysis
• Conceptual understanding of APIs & system integrations
• Agile / Scrum
• UAT & Solution Validation
• Functional & Technical Documentation

Good-to-Have Skills

• Exposure to Python or another programming language (helps you read what's technically feasible, not required to write production code).
• Familiarity with OpenAI, Azure OpenAI, AWS Bedrock, Google Vertex AI, or similar platforms.
• Awareness of LangChain, LlamaIndex, Semantic Kernel, or similar frameworks.
• Basic understanding of Vector Databases and Embeddings.
• Experience supporting AI/GenAI POCs (as the BA, not the builder).
• Experience with enterprise platforms such as SAP, Salesforce, ServiceNow, CRM, or ERP systems.
• Awareness of AI Governance, Responsible AI, and Data Privacy considerations.
• Previous experience in consulting or professional services.
• Domain knowledge in financial services — e.g., lending, payments, wealth management, insurance, or leasing/asset finance — including familiarity with relevant compliance and regulatory considerations (KYC, AML, data privacy in finance).