Automation Engineer | Security Assurance

Automation Engineer | Security Automation, Data Pipelines & Production AIPay: Up to $70/hrContract: W2 ContractDuration: 12 monthsSchedule: Remote within the U.S. | Monday–Friday, 40 hours/week | No overtime

Location: U.S.

RemoteStart: 10/19/2026This is not a traditional QA automation or RPA role.Swoon is partnering with a leading enterprise software company looking for an Automation Engineer to join its Security Assurance & Trust organization and help build two internal platforms supporting compliance and customer trust workflows.The team is looking for a hands-on engineer who can build reliable integrations and data pipelines across systems like AWS, Okta/Entra, Jira, GitHub, and other SaaS platforms — while also helping solve a newer engineering challenge: how do you make AI-generated output reliable enough to use in security and compliance processes?You'll work directly in the applications and existing codebase, building integrations, automated control checks, production features, and testing infrastructure. On the AI side, you'll create evaluation datasets, regression checks, guardrails, and structured outputs to measure whether LLM-generated responses are actually accurate and consistent.What you'll get to work on:

• 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.You'll be a strong fit if you have:
• 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. Experience with eval sets, regression testing, structured outputs, guardrails, or similar quality controls is particularly relevant.
• Strong technical documentation skills and the ability to leave behind systems another engineering team can understand and maintain.Nice to have: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.The biggest distinction here: this is a software engineering role centered on automation, integrations, and production reliability — not someone primarily building test automation scripts, RPA workflows, or one-off AI prototypes.You should be comfortable owning the plumbing between systems and thinking critically about whether automated and AI-generated outputs can actually be trusted.Important: This is a 12-month contract with no planned conversion to full-time. A personal laptop is required for the engagement.If your background sits at the intersection of software engineering, API/data integration, and production AI — particularly in security, compliance, or internal platforms — we encourage you to apply.