
Strategic AI Systems Designer
2 weeks ago
We are seeking a highly skilled Cloud AI Architect to fill this key role.
This is a challenging and rewarding position that requires designing and implementing cloud-based AI solutions to drive business growth and innovation.
Key Responsibilities:
- Designing AI-powered cloud infrastructures: Selecting appropriate cloud services, designing the architecture, and ensuring scalability and performance.
- Integrating AI models: Seamlessly incorporating AI models into existing cloud infrastructure and applications.
- Ensuring alignment with business strategy: Working with stakeholders to understand business requirements and translate them into technical solutions.
- Managing deployment and maintenance: Overseeing the deployment process, monitoring performance, and ensuring the long-term health of AI solutions.
- Identifying future AI needs: Anticipating future business requirements and planning for the evolution of AI systems.
- Collaboration with various teams: Working with data scientists, data engineers, and developers to implement AI solutions.
Requirements:
- Bachelor's or Master's degree in Computer Science, Artificial Intelligence, Data Science, or related field.
- 10-15 years of experience in cloud architecture with 5+ years specializing in AI/ML system design and deployment.
- Deep knowledge of AI/ML frameworks (TensorFlow, PyTorch, scikit-learn) integrated with cloud platforms.
- Proficient with cloud AI services: Azure AI (Cognitive Services, ML Studio), AWS AI/ML (SageMaker, Rekognition), Google AI Platform.
- Experience with MLOps tools (Kubeflow, MLflow, Azure MLOps).
- Strong programming skills in Python, R, or Java.
- Understanding of data engineering concepts and cloud data platforms.
- Familiarity with cloud security standards and data privacy regulations.
- Excellent leadership and stakeholder management skills.
Preferred Qualifications:
- Advanced AI/ML certifications - Microsoft Certified: Azure AI Engineer, AWS Certified Machine Learning Specialty, Google Professional Machine Learning Engineer.
- Experience with cutting-edge AI technologies like NLP, computer vision, reinforcement learning.
- Knowledge of emerging AI regulations and ethical AI standards.
- Experience with container orchestration (Kubernetes) for scalable AI deployments.
- Experience in hybrid and multi-cloud AI deployments.
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