Senior AI Solutions Architect

2 weeks ago


Charlotte, North Carolina, United States Calabitek Full time
Job Overview

Important: Please include identification documents during submission.

All candidates must possess a LinkedIn profile and be prepared to share it.

Core Responsibilities:

- Architect and design robust AI systems to create and implement AI solutions utilizing machine learning and deep learning methodologies.

- Collaborate with business stakeholders, data scientists, engineering teams, product managers, and other key partners to define functional and non-functional requirements for AI use cases on the AI Platform.

- Assess new technologies and tools in the AI domain and conduct fitment analysis to align with the Enterprise AI Platform and capabilities strategy.

- Establish and enforce best practices, frameworks, and standards for AI/ML architecture.

- Oversee the setup of AI/ML infrastructure, including the selection of cloud services, data pipelines, and model deployment strategies.

- Ensure the reliability, robustness, and scalability of AI/ML solutions in live environments.

- Implement data governance, security, and compliance protocols for AI/ML platforms.

- Enhance AI/ML workflows for optimal performance, cost-effectiveness, and resource utilization.

- Provide technical guidance and mentorship to AI/ML development teams.

- Articulate AI/ML architectural decisions and strategies to stakeholders and executive leadership.

Essential Qualifications:

- Demonstrated experience as an architect for AI/ML platforms.

- Profound knowledge of machine learning algorithms, deep learning architectures, models, and frameworks (e.g., TensorFlow, PyTorch, Scikit-Learn).

- Expertise in cloud computing platforms (e.g., GCP, Azure) and their associated AI services.

- Strong understanding of the model development lifecycle, software engineering principles, and data engineering practices.

- Experience with containerization and orchestration tools in both on-premises and cloud environments (e.g., AKS, GKE, OpenShift, Docker, Kubernetes) for deploying AI/ML models.

- Ability to design and optimize distributed computing systems tailored for AI/ML workloads.

- Familiarity with DevOps methodologies, CI/CD pipelines, and automation tools within AI/ML contexts.

- Exceptional problem-solving abilities to tackle complex technical challenges.

- Strong communication skills to facilitate collaboration with cross-functional teams and stakeholders.



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