Job Summary
Key Responsibilities: Discovery & Value Engineering Structure business problems and shape use‑case hypotheses. Define value drivers and KPIs. Conduct Impact × Complexity assessments. Prioritize, roadmap, and plan Pilot → Scale initiatives. Architecture & Feasibility Develop reference architectures and guardrails for Copilot, plugins/connectors, and retrieval patterns (RAG/indices). Perform feasibility analyses (data access, security, integration, compliance). Prepare architectural options and decision proposals. Delivery & Orchestration Design, implement and support end‑to‑end GenAI solutions (flows, agents, tools). Define handoffs between automation and GenAI components. Set up telemetry, monitoring, and cost controls (tokens, hosting, licensing). Security, Compliance & Responsible AI Define data classifications and privacy/PII protection measures. Implement policies for Responsible AI. Evaluate third‑party GenAI solutions and conduct due‑diligence reviews. Enablement & Change Conduct workshops, create playbooks and templates. Align stakeholders (Legal, Security, Works Council). Support adoption planning and rollout. Scorecard Scale: 1 = Basic · 2 = Practiced · 3 = Senior · 4 = Architect Level GenAI Platform Expertise (20%) M365 Copilot (Capabilities, adoption) — 3–4 Foundry SDK (development) 4 Azure OpenAI / Custom GPTs (system prompting, patterns) — 3–4 Evaluation of external GenAI vendors — 3 Cost models & TCO (tokens, hosting, licensing) — 3–4 Use‑Case Design & Value Engineering (20%) Problem framing, KPI definition — 4 Prioritization (Impact × Complexity) — 4 Pilot→Scale roadmapping, hypothesis testing — 3–4 Benefit case & adoption planning — 3–4 Solution Architecture & Feasibility (20%) Reference architectures (Copilot, plugins/retrievers) — 3–4 Feasibility (data, security, integration) — 4 Azure building blocks (Functions, Logic Apps, Key Vault) — 3 Operational model (monitoring, telemetry, SLAs) — 3–4 Automation vs. GenAI (10%) When to use RPA/Power Automate vs. GenAI — 4 Orchestration (flows, agents, handoffs) — 3–4 Guardrails (cost, quality, risk) — 3–4 Security/Compliance & Responsible AI (10%) Data protection (PII, IP) — 3–4 Responsible AI policies (safety, bias, audit) 3 Tenant & isolation concepts — 3 Third‑party due diligence
Key Responsibilities
Job Purpose:
Translate business requirements into GenAI use cases, assess technical feasibility, design scalable solutions on Microsoft platforms (primarily M365 Copilot and Azure OpenAI / Custom GPTs), and guide these solutions through end‑to‑end delivery.
A key aspect of the role is ensuring the right boundary and orchestration between Automation (Power Platform / RPA) and GenAI, always considering value, security, compliance, and scalability.
The ecosystem is primarily Microsoft‑based (Copilot, Azure). Other vendors (e.g. Salesforce Agentforce, SAP Joule) are evaluated where relevant.
Skill Requirements
Key Responsibilities:
Discovery & Value Engineering
- Structure business problems and shape use‑case hypotheses.
- Define value drivers and KPIs.
- Conduct Impact × Complexity assessments.
- Prioritize, roadmap, and plan Pilot → Scale initiatives.
Architecture & Feasibility
- Develop reference architectures and guardrails for Copilot, plugins/connectors, and retrieval patterns (RAG/indices).
- Perform feasibility analyses (data access, security, integration, compliance).
- Prepare architectural options and decision proposals.
Delivery & Orchestration
- Design, implement and support end‑to‑end GenAI solutions (flows, agents, tools).
- Define handoffs between automation and GenAI components.
- Set up telemetry, monitoring, and cost controls (tokens, hosting, licensing).
Security, Compliance & Responsible AI
- Define data classifications and privacy/PII protection measures.
- Implement policies for Responsible AI.
- Evaluate third‑party GenAI solutions and conduct due‑diligence reviews.
Enablement & Change
- Conduct workshops, create playbooks and templates.
- Align stakeholders (Legal, Security, Works Council).
- Support adoption planning and rollout.