Note: The job is a remote job and is open to candidates in USA. Swoon is partnering with a leading enterprise software company to support its Security Assurance & Trust organization. The Automation Engineer will build reliable integrations, evidence collection pipelines, automated security and compliance checks, and production AI evaluation infrastructure across cloud, identity, ticketing, source control, and enterprise SaaS systems.
Responsibilities
- Build API- and event-driven evidence collection pipelines across cloud, identity, ticketing, source control, endpoint, and other enterprise systems
- Turn security and compliance controls into executable automated checks that can evaluate source-system state and return pass/fail results with supporting evidence
- Engineer the data layer behind those integrations, normalizing information from different systems while maintaining timestamps, provenance, and traceability for audit purposes
- Handle the less glamorous but critical parts of production integrations — pagination, authentication, rate limits, retries, schema changes, partial failures, and recovery
- Build and test features directly within existing internal applications, working alongside the engineering lead and contributing through established code review and CI/CD processes
- Help harden AI-assisted workflows including questionnaire response generation and evidence-to-control mapping by creating eval datasets, scoring methodologies, regression tests, structured outputs, guardrails, and fallbacks
- Instrument production workflows with logging, monitoring, alerting, and dashboards to catch broken integrations, stale evidence, and AI output drift
- Document integration architecture, data models, automated control logic, AI evaluation methodology, and known limitations so the team can maintain the systems long-term
Skills
- 5+ years of hands-on software, platform, or automation engineering experience building and shipping production code
- Strong Python and/or TypeScript skills with real experience building REST/GraphQL integrations, webhooks, authentication flows, data pipelines, and automated tests
- Experience integrating multiple SaaS, identity, cloud, or enterprise systems — think AWS, Okta, Entra, Jira, GitHub, Google Workspace, or similar environments
- Strong data pipeline/ETL fundamentals, including pulling data from different source systems, normalizing it into consistent schemas, and maintaining traceability downstream
- Hands-on cloud API and IAM experience, ideally within AWS
- Experience working within an existing production codebase — reading someone else's code, following established patterns, writing tests, and safely shipping changes through CI/CD
- Production experience integrating LLM functionality into applications
- The team specifically needs someone who understands how to evaluate AI output, not simply call an LLM API
- Strong technical documentation skills and the ability to leave behind systems another engineering team can understand and maintain
- You should be comfortable owning the plumbing between systems and thinking critically about whether automated and AI-generated outputs can actually be trusted
- Experience with eval sets, regression testing, structured outputs, guardrails, or similar quality controls is particularly relevant
- Experience with security or GRC environments such as SOC 2, ISO 27001, FedRAMP, or HIPAA would be valuable, as would exposure to platforms or tooling like Vanta, Drata, Secureframe, OSCAL, Cloud Custodian, OPA/Rego, Prowler, or Steampipe
- Experience with Anthropic/Claude or similar LLM APIs, internal tooling, containerization, and CI/CD environments is also helpful
Benefits
- Remote within the U.S.
Company Overview