Description

Architect and design scalable AI platforms to deploy machine learning and deep learning solutions.

Responsibilities

• Lead AI/ML infrastructure setup, including cloud service selection and data pipeline design.
• Collaborate with stakeholders, data scientists, and engineering teams to define joint architecture requirements.
• Implement AI/ML architecture best practices, frameworks, standards, and data governance measures.
• Optimize AI/ML workflows for performance, cost efficiency, and resource utilization in production.
• Provide technical leadership and mentorship to development teams while communicating architecture decisions to executives.

Required Skills

• 5+ years of experience in AI/ML platform architecture.
• Deep understanding of ML algorithms and Deep Learning architectures.
• Hands-on experience with TensorFlow, PyTorch, and Scikit-learn.
• Expertise in Google Cloud Platform (GCP) and its AI services.
• Proficiency with containerization and orchestration: Docker, Kubernetes, GKE, AKS, and OpenShift Container Platform.
• Strong knowledge of the model development life cycle, software engineering, and data engineering principles.
• Experience designing and optimizing distributed computing systems for AI/ML workloads.
• Familiarity with DevOps practices and CI/CD pipelines in AI/ML contexts.

Key Skills

Education

Any Graduate