Enterprise AI Systems Architect

Socket.dev; Nevada (IA); On-site; USD 140,000 - 210,000; Full time

Job summary

enGen is seeking an AI Architect to shape the overall AI strategy and design scalable, secure systems that integrate with enterprise infrastructure. You will specify the high-level architecture for AI solutions, select technology stacks, and establish best practices for MLOps across engineering teams.

The role requires deep experience in distributed systems, cloud-native patterns, and governance for privacy and compliance, with cross-functional leadership across Data Science, DevOps, and Product

Qualifications

• Architectural expertise across distributed systems and cloud-native patterns.
• Experience with IaC tools (Terraform, Ansible) to manage AI infrastructure.
• Expertise in AI lifecycle tools (PyTorch/TensorFlow, Kubeflow, MLflow).
• Knowledge of Generative AI, LLMs, RAG, and model deployment strategies.

Responsibilities

• Lead strategic consultation to define business processes, functions, and organizational structures; develop enterprise architecture and roadmaps.
• Design and evaluate end-to-end AI architectures, data pipelines, and model delivery systems for scalability and security.
• Provide guidance on technology choices, design reviews, and alignment with business goals.
• Develop and communicate functional and non-functional requirements for complex AI systems.
• Mentor engineers and present architectural decisions to executive leadership.
• Collaborate across Data Science, DevOps, and Product teams to align AI initiatives with infrastructure capabilities.
• Support governance, data privacy, and AI safety considerations in system designs.

Education

Bachelor’s Degree in Information Technology or closely related field

Job description

enGen is seeking an AI Architect to shape the overall AI strategy and design scalable, secure systems that integrate with enterprise infrastructure. You will specify the high-level architecture for AI solutions, select technology stacks, and establish best practices for MLOps across engineering teams.

The role requires deep experience in distributed systems, cloud-native patterns, and governance for privacy and compliance, with cross-functional leadership across Data Science, DevOps, and Product