SR. DIGITAL ARCHITECT

Primary Purpose Of The Job The Senior Digital Architect is responsible for designing, implementing, and maintaining Cloud digital platforms across the organization. This role focuses on enhancing enterprise analytics capabilities by integrating AI/ML models, emerging technologies, and large-scale data processing frameworks to drive data-driven decision-making and operational efficiency. Additionally, the digital architect will collaborate with business and technical teams to accelerate digital transformation, streamline data governance, and adopt emerging technologies to drive continuous innovation. Principle Accountabilities • Design scalable, secure, and cloud-native architecture frameworks for AI/ML solutions, including MLOps pipelines, model deployment infrastructure, and integration patterns. • Establish architectural guidelines for implementing generative AI solutions, including safety measures and ethical considerations. • Design scalable architectures for packaging AI/ML models as production-ready APIs or custom applications/microservices. • Define technical standards for model deployment, monitoring, and maintenance. • Identify and evaluate industry-specific data and AI platforms, Auto-ML tools to fit business use cases and derive value. • Partner with business stakeholders to understand requirements and translate them into technical solutions • Establish governance frameworks for AI model lifecycle management • Assess technical and Information security risks and provide mitigation strategies in the implementation of digital solutions • Audit AI tools and practices across data, models and engineering, focusing on continuous improvement and feedback mechanisms. • Provide strategic and technical guidance to stakeholders regarding digital solutions, cloud architecture, and platform optimizations. • Stay updated on emerging technologies and apply cloud-native, AI/ML-driven solutions, automation tools to foster innovation in business processes. Required Experience And Skills AI/ML Expertise • 8+ years of experience in digital solution architecture, with at least 5 years focusing on AI/ML solutions preferably in the Energy Sector. • Proven track record of designing and implementing enterprise-scale AI/ML architecture/solutions. • 5+ years of experience with AI/ML frameworks (TensorFlow, PyTorch, etc.) and AI model deployment. • Proficient in large language models and generative AI implementations. • Experience in prompt engineering, RAG, and fine-tuning to optimize model performance and response accuracy. • Experienced with real-time ML systems and edge computing. MLOps and DataOps • Strong knowledge of MLOps practices and tools. • Proficiency in MLOps and DataOps methodologies, including CI/CD pipelines, ML model monitoring, and automation. • Experience with DevOps, serverless computing, and containerization (Docker, OpenShift, Kubernetes). Cloud and Architecture • Strong expertise in one of the cloud computing platforms (Azure, GCP), cloud services (SaaS, PaaS, DaaS), and microservices-driven architectures. • Experience in designing scalable architectures for packaging AI/ML models as production-ready APIs or custom applications/microservices. • Working knowledge of system integration approaches, modern data architectures, and cloud-native application design. Governance and Compliance • In-depth understanding of data governance frameworks, cybersecurity principles, regulatory compliance, and AI ethics in enterprise settings. • Knowledgeable in AI/ML regulatory compliance. Communication and Collaboration Excellent communication skills to effectively collaborate with business, technical, and product teams and translate complex technical requirements into actionable solutions Educational Qualifications • A degree in Information Management, Engineering, or a technology related field, demonstrating strong analytical and quantitative skills. • Advanced degrees (Master's or PhD) in Data Science, Applied Machine Learning, Computer Science, or Statistics is highly desirable Show more Show less

8+ years of experience in digital solution architecture, with at least 5 years focusing on AI/ML solutions preferably in the Energy Sector. Proven track record of designing and implementing enterprise-scale AI/ML architecture/solutions. 5+ years of experience with AI/ML frameworks (TensorFlow, PyTorch, etc.) and AI model deployment. Proficient in large language models and generative AI implementations. Experience in prompt engineering, RAG, and fine-tuning to optimize model performance and response accuracy. Experienced with real-time ML systems and edge computing. Strong knowledge of MLOps practices and tools. Proficiency in MLOps and DataOps methodologies, including CI/CD pipelines, ML model monitoring, and automation. Experience with DevOps, serverless computing, and containerization (Docker, OpenShift, Kubernetes). Strong expertise in one of the cloud computing platforms (Azure, GCP), cloud services (SaaS, PaaS, DaaS), and microservices-driven architectures. Experience in designing scalable architectures for packaging AI/ML models as production-ready APIs or custom applications/microservices. Working knowledge of system integration approaches, modern data architectures, and cloud-native application design. In-depth understanding of data governance frameworks, cybersecurity principles, regulatory compliance, and AI ethics in enterprise settings. Knowledgeable in AI/ML regulatory compliance. Educational qualifications include a degree in Information Management, Engineering, or a technology-related field; advanced degrees in Data Science, Applied Machine Learning, Computer Science, or Statistics are highly desirable.

Design, implement, and maintain cloud-native architectures for AI/ML solutions, including MLOps pipelines, model deployment infrastructure, and integration patterns. Establish architectural guidelines for generative AI solutions, including safety measures and ethical considerations. Design scalable architectures for packaging AI/ML models as production-ready APIs or microservices. Define technical standards for model deployment, monitoring, and maintenance. Identify and evaluate industry-specific data and AI platforms, Auto-ML tools to fit business use cases. Partner with business stakeholders to translate requirements into technical solutions. Establish governance frameworks for AI model lifecycle management. Assess technical and information security risks and provide mitigation strategies. Audit AI tools and practices across data, models, and engineering for continuous improvement. Provide strategic and technical guidance to stakeholders regarding digital solutions, cloud architecture, and platform optimizations. Stay updated on emerging technologies and apply cloud-native, AI/ML-driven solutions and automation to foster innovation.