We are looking for a Business Analyst Lead - Gen AI who will bridge the gap between Generative AI innovation and business value, driving adoption of tools like LLMs, RAG systems, and AI agents to solve complex enterprise challenges.

Technical & Tools

1.GenAI knowledge

• Hands‑on experience with LLM usage (LangChain, LlamaIndex).

• Understanding of RAG architectures, fine‑tuning (LoRA), and vector databases.

• Familiarity with tools: Azure OpenAI Studio, GCP Vertex AI, Hugging Face.

2.Business Analysis

• Advanced user story mapping for multi‑agent workflows (AutoGen, CrewAI).

• Process modeling (BPMN) for AI‑human collaboration.

3. Data Fluency

• SQL/Python basics to validate training data quality.

• Experience with synthetic data generation tools (Gretel, Mostly AI).

GenAI Opportunity Identification

• Partner with stakeholders to identify high‑impact GenAI use cases (e.g., chatbots, synthetic data, content automation).

• Conduct cost‑benefit analyses comparing build vs. buy (e.g., fine‑tuning Llama 3 vs. GPT‑4 Enterprise).

Requirement Engineering for GenAI

• Define non‑functional requirements unique to GenAI:

• Accuracy thresholds (e.g., <5% hallucination rate).

• Ethical guardrails (bias mitigation, PII redaction).

• Latency SLAs (e.g., <2s response time for RAG systems).

• Document prompt engineering guidelines and iteration workflows.

Stakeholder Collaboration

• Translate technical GenAI capabilities into business outcomes:

• “Reducing customer support costs by 30% via AI agents.”

• “Accelerating contract drafting by 50% with GPT‑4‑assisted tools.”

• Manage expectations around probabilistic outputs (e.g., fact‑checking workflows for GenAI content).

Performance & Governance

• Develop GenAI‑specific KPIs:

• Token cost per interaction, user trust scores, automation rate.

• Model drift detection and retraining triggers.

• Design audit frameworks for regulatory compliance (EU AI Act, industry standards).