Staff AI Engineer – Business Systems

About this role

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;…