Enterprise Architect – Cloud, Data & AI (Omani National)

About the role

We are looking for a seasoned Enterprise & Solution Architect to lead large‑scale digital transformation across telecom, energy, public sector and BFSI. The candidate will define enterprise architecture strategies, drive solution design and ensure alignment with business goals.

Key responsibilities

• Define and drive enterprise architecture strategy aligned with business objectives across multiple industry domains.
• Lead end‑to‑end solution architecture for complex transformation programs, ensuring scalability, security and performance.
• Design data‑driven architectures, including data platforms, analytics ecosystems and AI/ML solutions.
• Architect solutions leveraging cloud platforms (OCI, AWS, Azure, GCP), microservices, APIs and modern web technologies.
• Promote adoption of AI capabilities such as machine learning, generative AI and emerging agentic AI frameworks.
• Establish architecture governance, standards and best practices across programs.
• Collaborate with business stakeholders to translate requirements into scalable architecture blueprints.
• Provide technical leadership to delivery teams and ensure alignment with the architecture vision.
• Evaluate and recommend technology platforms, tools and vendors.
• Lead solutioning for RFPs, proposals and client engagements, including effort estimation and technical strategy.

Required profile

• 12+ years of experience in enterprise and solution architecture.
• Strong cross‑domain expertise in at least two of: telecom (BSS/OSS), energy & utilities, public sector (eGov, smart city), BFSI.
• Proven ability to design large‑scale, multi‑cloud solutions.
• Bachelor’s or Master’s degree in Computer Science, Engineering or related field.
• Architecture certifications such as TOGAF or cloud architect credentials are preferred.

Required skills

• Enterprise and solution architecture
• Cloud architecture (AWS, Azure, GCP, OCI)
• Microservices, event‑driven, API‑first and domain‑driven design
• Modern front‑end and back‑end web frameworks
• Integration technologies (APIs, middleware, ESB, iPaaS)
• Data architecture (data lakes, data warehouses, lakehouse)
• Machine learning, MLOps and AI lifecycle management
• Generative AI (LLMs, prompt engineering, RAG)
• Agentic AI concepts
• Data governance, security and privacy frameworks