AI Engineer: Enterprise Platform Architect

Cerebras Systems builds the world's largest AI chip, 56 times larger than GPUs. This architecture allows Cerebras to deliver industry-leading training and inference speeds; over 10 times faster than GPU-based hyperscale cloud inference services.

This order of magnitude increase in speed is transforming the user experience of AI applications, unlocking real-time iteration and increasing intelligence via additional agentic computation.

Cerebras works with the leading model labs, global enterprises, and cutting-edge AI-native startups. OpenAI recently announced a multi-year partnership with Cerebras, to deploy 750 megawatts of scale, transforming key workloads with ultra high-speed inference.

Hands-on AI engineering, enterprise automation and agile delivery for Finance, operations and business systems

About the role

The AI and Automation Engineer is a hands-on member of the Business Intelligence and Systems team responsible for designing, building and operating secure AI solutions and enterprise automations for Cerebras. The role will translate complex Finance, Accounting, Supply Chain and enterprise requirements into reliable agents, applications, integrations and controlled workflows.

This engineer will serve as a technical owner across the AI and automation lifecycle. The role combines solution architecture with direct engineering, disciplined production support, and delivery through Scrum and Agile practices. Success requires strong software-engineering judgment, practical business-process knowledge and the ability to move a prototype into a secure, tested and supportable production service.

What you will do

AI solution architecture and engineering

• Design, build, test, deploy and support production AI agents, orchestration services, enterprise applications and reusable platform components.

• Create reusable patterns for agents, tools, APIs, Model Context Protocol servers, prompts, retrieval, evaluations and human-review workflows.

• Design and support secure connections between AI services and approved enterprise platforms, beginning with Net Suite and extending to data, procurement, contracts, HR, CRM and other systems as priorities evolve.

• Build reliable integrations using APIs, MCP, webhooks, event-driven services, SFTP and enterprise integration platforms where appropriate.

• Own operational requirements for internal and third-party components, including credential and key rotation, access reviews, patching, monitoring, incident response and recovery.

AI platform evaluation and production reliability

• Evaluate models, agent frameworks, connectors and enterprise platforms through structured proofs of concept and documented technical recommendations.

• Assess accuracy, security, reliability, integration fit, user experience, latency, operating cost and vendor viability before production adoption.

• Build evaluation datasets, automated and human grading methods, release thresholds and ongoing monitoring for deployed solutions.

• Diagnose production failures, identify root causes and implement durable improvements to code, prompts, data, controls and operating procedures.

Business process solutions and adoption

• Partner with Finance, Accounting, Supply Chain, Business Operations, IT and Security to identify high-value use cases and translate requirements into controlled solutions.

• Deliver solutions for close and reporting, forecasting, procurement, billing, compliance monitoring and other enterprise workflows where automation provides measurable value.

• Establish clear business ownership, user feedback loops, training and adoption plans for every production workflow.

• Use task success, accuracy, business-process cycle time, adoption, trust and support demand to guide iteration and determine…