Staff AI Engineer – Business Systems

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, solution architecture and compliance-by-design for enterprise Finance, operations and business systems ROLE SCOPE Responsibilities The role is accountable for hands-on delivery and architecture within its layer, with shared governance across BIS, Finance, Business Operations, IT and Security and active partnership with other enterprise functions. AI solution architecture Design end-to-end agentic solutions and determine when a use case should query a source system directly versus use the unified data model. Partner with stakeholders to identify high-value use cases, translate requirements into controlled AI workflows and select AI, conventional automation or no new technology. Create reusable architecture patterns for agents, tools, APIs, MCP servers, prompts, evaluations and human-review workflows. Produce solution designs, security flows, deployment patterns and technical standards. AI engineering and system enablement Build AI agents, orchestration services, enterprise applications and reusable platform components. Deliver workflows for close and reporting, procurement, forecasting, billing and compliance monitoring where AI adds measurable value. Establish secure, primarily read-only AI connections to approved business systems, beginning with NetSuite and extending to adjacent Finance and enterprise platforms as priorities evolve. Preserve source-system authentication, authorization, user-level entitlements, rate limits and audit trails. Implement citations, evidence links, deterministic checks, exception handling and safe action boundaries. Prototype-to-enterprise delivery Assess business-built or rapidly developed prototypes for value, architecture, security, maintainability and control readiness. Refactor or rebuild approved prototypes into tested, monitored and supportable enterprise applications. Establish development, test and production environments, release pipelines, incident response and rollback controls. AI platform strategy Evaluate AI models, agent frameworks, connectors and enterprise platforms on a regular cadence. Run structured proofs of concept and assess security, accuracy, integration, scalability, experience, cost and vendor viability. Maintain platform standards and recommend adoption, retention, replacement or retirement decisions. Organizational enablement and adoption Create clear documentation, reusable patterns and reference architectures; coach teams on effective agent design, prompts, evaluation practices and safe operating boundaries. Establish feedback loops with users and process owners; use adoption, task success, efficiency, trust and support signals to guide iteration. Finance, SOX and compliance Translate Finance, Security, Privacy, SOX and SSDLC requirements into technical architecture and application controls. Implement least privilege, segregation of duties, logging, retention, evaluation, change control and audit evidence. Require deterministic validation and reconciliation for financially material outputs. Support SOX walkthroughs, control testing, audits, risk assessments and remediation while escalating formal approval to control owners. CANDIDATE PROFILE Qualifications, success measures and boundaries Required capabilities are calibrated for a Staff-level hands-on