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).