Senior Digital Architect

Overview

The role of Senior Digital Architect focuses on designing, deploying, and maintaining cloud-based digital platforms to foster organizational data analytics capabilities. This involves integrating advanced AI/ML models, cutting-edge technologies, and large-scale data processing frameworks to enhance decision-making processes and operational efficiency. The position requires close collaboration with business and technical teams to drive digital transformation, ensure proper data governance, and continuously innovate through emerging technology adoption.

Key Responsibilities

• Create scalable, secure cloud-native architectures tailored for AI/ML initiatives, including MLOps pipelines, deployment infrastructure, and system integration.
• Develop guidelines for generative AI implementations emphasizing safety and ethical standards.
• Design architectures that transform AI/ML models into production-ready APIs or microservices.
• Set and enforce technical criteria for model deployment, performance monitoring, and ongoing maintenance.
• Evaluate and select industry-specific data and AI platforms, as well as automated ML tools, to align with organizational goals.
• Collaborate with stakeholders to translate business needs into technical solutions.
• Implement governance frameworks overseeing the AI model lifecycle.
• Assess and mitigate information security and technical risks in digital solution implementation.
• Conduct audits on AI tools and engineering processes to promote continuous improvement.
• Offer strategic and technical recommendations on digital solutions and cloud architecture optimizations.
• Stay abreast of emerging technologies to leverage cloud-native AI/ML solutions and automation tools for business innovation.

Experience and Skills Required

• Over 8 years in digital solution architecture with a minimum of 5 years focused on AI/ML, preferably within the energy sector.
• Proven expertise implementing enterprise-scale AI/ML architectures.
• Five-plus years hands-on with AI/ML frameworks like TensorFlow and PyTorch and experience deploying AI models.
• Strong background in large language models, generative AI, prompt engineering, Retrieval-Augmented Generation (RAG), and fine-tuning techniques.
• Experience with real-time machine learning systems and edge computing applications.
• Comprehensive knowledge of MLOps and DataOps methodologies, including CI/CD pipelines, model monitoring, and automation workflows.
• Proficient with DevOps practices, serverless computing, and container platforms such as Docker, OpenShift, and Kubernetes.
• Expertise in at least one public cloud platform (Azure or GCP) and familiarity with SaaS, PaaS, and DaaS services plus microservices architectures.
• Understanding of modern data architectures, system integration, and cloud-native application designs.
• Deep awareness of data governance, cybersecurity best practices, regulatory compliance, and AI ethics.
• Ability to communicate effectively across technical, business, and product teams to translate complex concepts into practical solutions.

Educational Qualifications

• Degree in Information Management, Engineering, or related technology fields showing strong analytical and quantitative background.
• Advanced qualifications such as a Master's or PhD in Data Science, Applied Machine Learning, Computer Science, or Statistics are highly desirable.