Staff AI Engineer – Business Systems

Role details

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 https://openai.com/index/cerebras-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 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 engineer with solution-architecture responsibilities. Required qualifications - 8+ years in software, platform, integration, solution engineering or enterprise applications, including meaningful hands-on production ownership in complex environments. - Strong Python and/or TypeScript skills; experience with APIs, MCP or comparable tool protocols, enterprise authentication and distributed-system design. - Practical experience building production AI systems using agents, tool use, retrieval, structured outputs, evaluations and monitoring. - Practical familiarity with leading LLM platforms and agent frameworks, such as OpenAI, Anthropic, Gemini, LangChain, Semantic Kernel or comparable technologies, including prompt and context engineering. - Strong solution-architecture judgment across security, reliability, performance, cost, observability and supportability. - Working knowledge of enterprise Finance processes such as general ledger, close, reporting, procure-to-pay, order-to-cash, forecasting and management reporting. - Working knowledge of compliance-by-design, including access, segregation of duties, change management, interfaces, automated controls, completeness and accuracy, and audit evidence. - Ability to communicate with engineers, Finance leaders, control owners, Security and executives. Preferred qualifications - Experience with ERPs, data platforms, frontier AI platforms, agent frameworks or comparable enterprise technologies. - Experience building internal enterprise applications. - Hands-on experience implementing SOX controls or operating in a public-company or audit-regulated environment. Success measures - Time from approved use case to controlled production and sustained adoption, with evidence of measurable business value. - Reduction in manual effort and business-process cycle time; improvement in decision quality or service levels. - Accuracy, groundedness, reconciliation suc