SR. DIGITAL ARCHITECT
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Key skills for this role
About the Role
QatarEnergy is looking for a Senior Digital Architect to design, implement, and maintain cloud digital platforms, focusing on AI/ML integration, large-scale data processing, and digital transformation.
Key Skills for This Role
Responsibilities
- Design scalable, secure, and cloud native architecture frameworks for AI/ML solutions
- Establish architectural guidelines for implementing generative AI solutions
- Design scalable architectures for packaging AI/ML models as production ready APIs or microservices
- Define technical standards for model deployment, monitoring, and maintenance
- 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
- Provide strategic and technical guidance to stakeholders regarding digital solutions
Requirements
- 8+ years of experience in digital solution architecture, with at least 5 years focusing on AI/ML solutions
- 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
- Strong knowledge of MLOps practices and tools
- Strong expertise in one of the cloud computing platforms (Azure, GCP)
- In depth understanding of data governance frameworks, cybersecurity principles, regulatory compliance, and AI ethics
- A degree in Information Management, Engineering, or a technology related field
Full Job Posting
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.
- Additionally, the digital architect will collaborate with business and technical teams to accelerate digital transformation, streamline data governance, and adopt emerging technologies.
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
- 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.
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.
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