Proficient AI COE

Role Purpose

To manage and governing discovery, delivery, and governance of enterprise AI across the organization, ensuring AI initiatives are business-outcome driven, responsibly governed, secure, and successfully adopted at scale. The role spans use‑case portfolio management, solution incubation, MLOps/LLMOps, and value realization through measurable KPIs.

Position Information

Title: Proficient, AI (COE)

• Unit: Strategy and Innovation
• Division: Center of Excellence
• Location: Muscat
• Grade:
• Proficient: P5
• Minimum role requirements:
• Qualifications: Bachelor’s degree in Computer Science, Engineering, Data Science, or related field; advanced degree preferred. Certifications in cloud AI/ML (e.g., Azure AI Engineer, AWS Machine Learning, Google Professional ML Engineer) and Responsible AI are a plus.
• Experience:
• Proficient: A minimum of 7 years’ experience
  

Key Accountabilities

• Drive discovery and prioritization of AI/ML use cases aligned to business value and strategic goals.
• Govern intake, triage, and planning of AI initiatives with clear ROI and benefit tracking.
• Conduct research on emerging AI technologies, including GenAI, RAG, vector search, and evaluation frameworks, to guide practical adoption.
• Translate business problems into AI solution designs, ensuring data readiness and ethical considerations.
• Lead PoCs with defined success criteria and scale successful pilots into production using MLOps/LLMOps practices.
• Partner with business units to embed AI into processes and ensure adoption through enablement and change management.
• Identify opportunities for AI-driven optimization, such as predictive analytics and generative solutions for knowledge work.
• Develop reusable AI components, prompt libraries, and playbooks to accelerate delivery.
• Monitor AI solution adoption using telemetry and feedback loops; continuously improve post-deployment.
• Implement model observability, including drift detection, hallucination monitoring, latency, and cost metrics.
• Maintain a value ledger with KPIs and FinOps metrics to measure business impact and cost efficiency.
• Facilitate governance ceremonies for intake, risk reviews, and value realization checkpoints.
• Conduct lifecycle reviews, bias testing, and Responsible AI audits for deployed models.
• Establish CI/CD pipelines for models, feature stores, and automated evaluation workflows.
• Enforce Responsible AI principles, privacy by design, and prompt/content safety for GenAI solutions.
• Manage incident response for AI pipelines, including rollback and root cause analysis.
• Design observability dashboards covering model quality, adoption, cost, and compliance metrics.
• Build AI productization business cases with TCO, compliance costs, and time-to-value considerations.
• Promote AI literacy, safe usage, and Responsible AI practices across the organization.