AI Solution Architect

Responsibilities:

• Co‑create AI vision, KPI tree, and prioritized use‑case portfolio with business leaders. 

• Translate strategy to a delivery roadmap and budget, with explicit risk and dependency plans. 

• Delivery leadership 

• Lead cross‑functional pods (data, platform, app, safety, SRE) from discovery through production. 

• Design A/B and canary rollout strategies; enforce guardrails and incident playbooks. 

• AI system engineering 

• Architect and guide implementation of LLM/RAG/agent solutions, including retrieval quality, prompt/policy engineering, guardrails, and evaluation harnesses. 

• Drive observability (tracing, safety counters, cost telemetry) and SLO compliance. 

• Governance and security 

• Stand up policy‑as‑code, model/prompt versioning, access controls, data residency, and vendor risk assessments. 

• Chair or collaborate with the governance board; run review gates. 

• Stakeholder engagement 

• Communicate simply and often; convert ambiguity into decisions; manage expectations. 

• Run demos, evidence‑based decisions, and post‑incident reviews. 

• Talent enablement 

• Mentor teams on AIOps/SRE practices; cultivate champions; reduce burden through automation. 

Must‑have skills:

• Leadership and ownership 

• Operates with high autonomy, bias to action, and accountability for outcomes. 

• Proven ability to align executives and guide cross‑functional teams without formal authority. 

• Communication and influence 

• Excellent written and verbal communication and meeting facilitation skills. 

• Translates technical topics (LLMs, safety, SOs) into business terms and tradeoffs. 

• Product and delivery thinking 

• Evidence‑driven decisions; comfort with A/B testing, canary rollouts, and ROI models. 

• Governance and security mindset 

• Practical understanding of data governance, privacy, and AI safety guardrails; policy‑as‑code. 

• Hands‑on AI systems integration 

• Experience integrating GenAI (LLMs/RAG/KG/agents) into real products with telemetry, guardrails, and rollback. 

• AIOps and reliability fundamentals 

• SLI/SLO design, error budgets, incident management, observability, CI/CD for prompts/policies/indexes. 

• Manufacturing/OT/IoT/Edge AI experience; familiarity with device data and shop‑floor constraints. 

• Microsoft Azure: Azure OpenAI, Cognitive Search, API Management, App Service/AKS, Functions, Event Hubs, Key Vault, Monitor; Azure ML or equivalent. 

Nice‑to‑have skills 

• SAP ecosystem awareness (SAP/S4H processes and integration points for AI governance). 

• Data and knowledge systems 

• Knowledge graphs/ontologies, hybrid retrieval (vector + keyword), embeddings, data contracts. 

• Safety and compliance 

• Red‑teaming methods, PII/PHI handling, content moderation pipelines, audit trails. 

• Cost and performance engineering 

• token/call cost controls. 

Experience profile 

• 7–12+ years in software/AI product delivery with 3+ years leading cross‑functional initiatives. 

• Track record of shipping AI or data‑intensive systems to production in enterprise settings. 

• Demonstrated practice of Site Reliability Engineering (SRE)/AIOps concepts (SLOs, incident response, observability).