AI and Automation Engineer - Business Systems

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.

• 8+ years of experience in software, platform, integration, solution engineering, enterprise applications or related technical roles, with meaningful hands-on production ownership in complex environments.
• Strong Python and TypeScript or JavaScript skills, with practical experience building APIs, services and enterprise applications.
• Hands-on experience building production AI systems using agents, tool use, retrieval, structured outputs, evaluations and monitoring.
• Practical experience with leading LLM platforms and agent frameworks such as OpenAI, Anthropic, Gemini, LangChain, Semantic Kernel or comparable technologies.
• Experience designing integrations with MCP or comparable tool protocols, enterprise authentication, permission-aware access and distributed-system reliability patterns.
• Demonstrated ability to design and build complete solutions rather than only evaluate vendors, administer platforms or produce prototypes.
• Working knowledge of enterprise Finance processes including close, reporting, procure to pay, order to cash, forecasting and management reporting.
• Experience working in Scrum or Agile teams and translating requirements into appropriately sized stories, estimates, acceptance criteria, test plans and releases.
• Working knowledge of software engineering practices including version control, peer review, environment management, automated testing, CI/CD and observability.
• Strong analytical, troubleshooting and communication skills, with the ability to work effectively with business leaders, engineers, Security, control owners and external partners.

• Experience building AI or automation solutions for Finance, Accounting, Supply Chain or other controlled enterprise processes.
• Experience integrating AI services with ERP, data, procurement, contract, HR, CRM or enterprise workflow platforms.
• Experience with cloud platforms, event-driven or serverless architectures and enterprise integration platforms.
• Experience implementing SOX controls or operating in a public-company, IPO-readiness or audit-regulated environment.
• Experience in semiconductor, hardware, manufacturing or another operationally complex environment.

• 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 NetSuite 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.

• 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.

• 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 whether a solution should scale.

• Deliver work through a Scrum-based operating model, participating actively in backlog refinement, sprint planning, daily stand-ups, estimation, demonstrations and retrospectives.
• Convert business needs into epics, user stories, technical tasks, acceptance criteria, evaluation plans and sequenced releases.
• Break complex initiatives into testable increments and communicate scope, dependencies, risks, tradeoffs and delivery status clearly.
• Maintain a strong definition of done covering design approval, peer review, testing, security and control validation, documentation, deployment readiness and operational handoff.

• Translate Security, Privacy, SOX and SSDLC requirements into technical architecture, application controls and operating procedures.
• Implement least privilege, segregation of duties, logging, retention, change control, evidence collection and periodic access review.
• Require deterministic validation and reconciliation when AI or automation touches financially material data or calculations.
• Support control walkthroughs, testing, audits, risk assessments and remediation while preserving formal approval by the appropriate control owner.

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, enterprise automation and agile delivery for Finance, operations and business systems

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:

• Build a breakthrough AI platform beyond the constraints of the GPU.
• Publish and open source their cutting-edge AI research.
• Work on one of the fastest AI supercomputers in the world.
• Enjoy job stability with startup vitality.
• Our simple, non-corporate work culture that respects individual beliefs.

Find out more about what it's like to work at Cerebras here https://www.cerebras.ai/join-us!

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

This website or its third-party tools process personal data. For more details, click here https://www.cerebras.net/privacy/ to review our CCPA disclosure notice.