# Staff AI Engineer – Business Systems

**Company:** [Cerebras Systems](https://hotfix.jobs/companies/cerebras-systems)
**Location:** Sunnyvale, CA
**Role:** ML Engineering
**Experience:** 8+ years
**Skills:** Python, TypeScript, APIs, Mcp, Distributed Systems, AI Agents, Retrieval-Augmented Generation, Llm Platforms, LangChain, Semantic Kernel, Prompt Engineering, NetSuite, Sox Controls, Enterprise Authentication, Monitoring
**Posted:** 2026-09-02

> Staff AI Engineer responsible for architecting and delivering governed AI agents, enterprise applications, and integrations across Finance and business systems. The role requires 8+ years of production engineering experience, strong Python or TypeScript skills, enterprise architecture judgment, and familiarity with AI platforms, agent frameworks, and compliance controls.

## Job Description

## Responsibilities

### AI solution architecture
- Design end-to-end agentic solutions and determine when use cases should query source systems directly versus use a unified data model.
- Partner with stakeholders to identify high-value use cases and translate requirements into controlled AI workflows.
- Select AI, conventional automation, or no new technology based on the use case.
- 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.
- Establish secure, primarily read-only AI connections to approved business systems, beginning with NetSuite and extending to adjacent Finance and enterprise platforms.
- 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 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.
- Run structured proofs of concept assessing security, accuracy, integration, scalability, user experience, cost, and vendor viability.
- Maintain platform standards and recommend adoption, retention, replacement, or retirement decisions.

### Organizational enablement
- Create documentation, reusable patterns, and reference architectures.
- Coach teams on agent design, prompts, evaluation practices, and safe operating boundaries.
- Establish user and process-owner feedback loops and 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.

## Requirements
- 8+ years of experience 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.
- Familiarity with leading LLM platforms and agent frameworks such as OpenAI, Anthropic, Gemini, LangChain, Semantic Kernel, or comparable technologies.
- Experience with prompt and context engineering.
- Strong solution-architecture judgment across security, reliability, performance, cost, observability, and supportability.
- Working knowledge of enterprise Finance processes, including 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.

## Nice-to-haves
- 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 working in a public-company or audit-regulated environment.

## Success measures
- Time from approved use case to controlled production and sustained adoption, with 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.
- Reuse of approved architecture patterns and components.
- 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.

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