AI Solution Architect Lead

Architectural Design: Develop and maintain the overall AI architecture, ensuring it is scalable, secure, and aligned with the organization's business goals. Provide architectural blueprints and technical leadership to AI teams across HQ and OpCos.

Solution Development: Design end-to-end AI solutions that integrate data processing, machine learning, and AI models into the existing business infrastructure, ensuring alignment with both technical and business requirements.

Technology Evaluation: Stay up to date with emerging AI technologies and tools. Evaluate and recommend new tools and platforms that can enhance AI performance and scalability within the organization.

Collaboration: Work closely with AI Engineers, Data Scientists, and Business Analysts to ensure that AI models are well-integrated into the business process and technical ecosystem, considering both local (OpCo) and global (HQ) needs.

Technical Leadership: Provide technical guidance and mentorship to AI Engineers and Data Engineers. Ensure best practices for AI model design, deployment, and scalability are followed.

System Integration: Ensure seamless integration of AI systems with other enterprise systems (e.g., CRM, ERP, marketing platforms). Oversee technical execution to ensure that the system design and AI solutions are properly implemented.

Security and Compliance: Ensure that AI solutions meet the organization's security and compliance requirements, including data governance, privacy regulations, and ethical AI guidelines.

Performance Optimization: Continuously monitor the performance of AI systems and identify opportunities to optimize architecture and model efficiency.

Experience:

1. 7+ years of experience in solution architecture or related roles, including 3+ years of experience in AI or data-intensive projects.
2. Proven experience designing and implementing scalable AI or machine learning architectures.
3. Strong understanding of cloud-based AI solutions (e.g., AWS, Google Cloud, Azure) and AI-related tools (e.g., TensorFlow, PyTorch).
4. Experience in systems integration and working with large-scale enterprise infrastructure.
5. Experience in telecommunications or a related industry.

Qualifications:

1. Bachelor's degree in computer science, Engineering, or related field (Master's degree preferred).
2. Experience with MLOps and AI lifecycle management tools.
3. Strong knowledge of cloud infrastructure and AI service providers.
4. Experience with AI system performance optimization techniques.