Remote Automation Engineer - AI-Driven Compliance Pipelines

Actalent; Bellevue (KY); Remote; USD 96,000 - 103,000; Full time

Benefits offered by this job

Medical, dental & vision

401(k) retirement plan

Life Insurance

Disability coverage

Job summary

Actalent is seeking an Automation Engineer for a fully remote, 12-month contract based in the United States. The role focuses on designing, building, and maintaining automated evidence collection and control-testing pipelines that integrate with cloud, identity, endpoint, ticketing, and source-control systems.

You will codify control requirements as executable tests, normalize data into consistent schemas, and ensure evidence flows into the internal controls platform, while hardening AI-assisted

Qualifications

• 5+ years of software or automation engineering experience.
• Proficient in Python and TypeScript for production software.
• Experience with API development including webhooks and auth.
• AWS IAM and cloud API usage in production environments.
• SaaS/integration experience with Okta/Entra, Jira, GitHub, Google Workspace.

Responsibilities

• Design and build automated evidence collection pipelines integrating with APIs and event-driven systems.
• Implement continuous control tests as code and surface results and evidence.
• Engineer the data layer by normalizing data and preserving provenance.
• Develop features in existing codebase following CI/CD processes.
• Harden AI workflows with evaluation datasets and guardrails, ensuring accuracy.

Skills

Python

TypeScript

API development

AWS IAM

Okta/Entra

Jira

GitHub

Google Workspace

ETL pipelines

CI/CD

LLM integration

Documentation

Job description

Actalent is seeking an Automation Engineer for a fully remote, 12-month contract based in the United States. The role focuses on designing, building, and maintaining automated evidence collection and control-testing pipelines that integrate with cloud, identity, endpoint, ticketing, and source-control systems.

You will codify control requirements as executable tests, normalize data into consistent schemas, and ensure evidence flows into the internal controls platform, while hardening AI-assisted