Staff Operations AI Engineer

About the Role

We're looking for a Staff Operations AI Engineer who will architect, build, and refine the intelligent systems that power Checkr’s AI-driven operations. In this role, you'll be the technical expert responsible for designing the integrations, workflows, and data structures that enable AI Agents and automation to operate reliably at scale.

You’ll work across system integration, workflow orchestration, application logic, data transformation, AI guardrails, and multi-platform automation—translating leadership’s strategic direction into high-quality technical execution. You’ll own complex, high-impact projects end-to-end, serve as a technical authority across teams, and develop robust systems that materially improve operational efficiency and AI performance.

What You’ll Do

AI Agent Integration & Automation Architecture

Design and own the integration architecture that enables AI Agents to operate safely and reliably across Checkr systems and third-party platforms.

Build production-grade API integrations with secure authentication flows, webhook and event-driven patterns, and robust automation workflows that coordinate actions across tools like Zendesk, Salesforce, and internal services.

Ensure AI-driven operations are resilient through strong error handling, retries, observability, and fallback mechanisms.

Proactively identify integration gaps, system bottlenecks, and failure modes, and lead the technical solutions that improve reliability and scale.

Systems Engineering & Data Foundations

Build and maintain the technical foundations that power AI-driven workflows, including structured data pipelines, normalized schemas, and predictable JSON inputs for LLMs.

Establish engineering standards for workflow design, code quality, and system observability.

Communicate architecture clearly through documentation and diagrams, and serve as a technical authority across teams to ensure consistent, high-quality execution.

AI Quality, Safety & Guardrails

Implement guardrails, validation rules, and safety checks that ensure AI Agents act responsibly and accurately in production.

Evaluate model output quality and continuously refine prompts, transformations, and logic to improve consistency, reliability, and trust in AI-driven decisions.

What You Bring

Required Experience

5-7+ years in automation platforms, integration architecture, or AI-enabled operations

Deep expertise in API design, OAuth/token-based authentication, webhooks, and event-driven systems

Proven experience building reliable automation workflows with observability, retries, and failure handling

Strong JavaScript and Python expertise for backend logic, scripting, and internal tooling

Solid SQL experience for data transformation, validation, and operational analytics (Snowflake a plus)

Advanced comfort with JSON, schemas, and data normalization for LLM and automation use cases

Hands-on experience running LLM-powered agents or automations in production, including guardrails and output validation, with proven experience in prompt engineering frameworks

Familiarity with integrating with CRM and support systems (e.g., Salesforce, Zendesk)

An A-player mindset with a strong bias for action: you raise the bar, move with urgency, stay resilient through ambiguity, and take ownership to deliver meaningful outcomes

Systems & Architecture

Track record designing end-to-end integration architectures across multiple platforms

Ability to translate complex operational needs into scalable, maintainable technical systems

Experience in anticipating API changes, schema drift, and downstream impacts in production environments

Mindset & Leadership

Systems thinker who understands how data, automation, and AI interact across the stack

Strong sense of ownership and commitment to building reliable, testable, observable systems

High bar for engineering rigor, with sound judgment around AI safety, governance, and compliance

Clear communicator who collaborates effectively across technical and non-technical partners

Role model of Checkr’s values; mentors others and contributes to a healthy, inclusive team culture

Nice-to-Have Experience

Direct experience with Tray.io / Make / Zapier / UiPath

Programming experience in Python

Snowflake specifically (vs strong SQL generally)

Salesforce or Zendesk integrations

Front-end UI widgets / embeds

Prior work in operations technology specifically (vs platform, infra, or product engineering)

Pay Transparency Disclosure

We use geographic cost of labor as an input to develop ranges for our roles and as such, each location where we hire may have a different range. If this role is remote, we have listed the top to the bottom of the possible range, but we will specify the target range for an exact location when you are selected for a recruiting discussion. For more information on our compensation philosophy, see our website.

On-target Earnings OR Base Salary range (Denver, CO)

$150,000—$176,000 USD

On-target Earnings OR Base Salary range (Nashville, TN)

$136,000—$159,980 USD