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

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 success and production reliability of deployed agents.

• User adoption, task success, stakeholder trust and support burden for production workflows.

• Latency, operating cost and cost per successful task for deployed agents and applications.

• Reuse of approved architecture patterns and components across use cases.

• Number of viable prototypes transitioned into governed enterprise solutions.

• Security, SOX and audit findings; evidence completeness; incident rate and remediation time.

• Quality and timeliness of AI platform evaluations and roadmap recommendations.

Why Join Cerebras

People who are serious about software make their own hardware. At Cerebras, we have built a breakthrough architecture that is unlocking new opportunities for the AI industry. With dozens of model releases and rapid growth, we’ve reached an inflection point in our business. Members of our team tell us there are five main reasons they joined Cerebras:

1. Build a breakthrough AI platform beyond the constraints of the GPU.

2. Publish and open source their cutting-edge AI research.

3. Work on one of the fastest AI supercomputers in the world.

4. Enjoy job stability with startup vitality.

5. Our simple, non-corporate work culture that respects individual beliefs.

Find out more about what it's like to work at Cerebras here!

Apply today and become part of the forefront of groundbreaking advancements in AI!

Cerebras Systems is committed to creating an equal and diverse environment and is proud to be an equal opportunity employer. We celebrate different backgrounds, perspectives, and skills. We believe inclusive teams build better products and companies. We try every day to build a work environment that empowers people to do their best work through continuous learning, growth and support of those around them.

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