Overview
TalentReach is hiring on behalf of a client seeking a hands-on Security Automation Engineer to help build and enhance internal platforms supporting security compliance, audit readiness, and customer trust. This is a remote, U.S.-based contract role.
The Security Automation Engineer will design automated evidence collection pipelines, develop continuous compliance checks, build integrations with cloud and SaaS platforms, and contribute directly to internal security and compliance applications. You will also help improve AI-assisted workflows by developing evaluation datasets, regression testing, structured outputs, and guardrails to ensure generated results can be trusted before reaching auditors, customers, or internal teams.
What You'll Do
- •Design and build automated evidence collection pipelines across cloud, identity, endpoint, ticketing, source control, and other enterprise systems
- •Develop API and event-driven integrations that minimize manual compliance and evidence collection work
- •Build integrations with platforms such as AWS, Okta, Microsoft Entra, Jira, GitHub, and similar systems
- •Convert compliance control requirements into executable automated tests
- •Run continuous control checks on scheduled intervals or in response to system events
- •Surface control results and supporting evidence within internal compliance platforms
- •Build and maintain the data layer supporting compliance evidence and control testing
- •Normalize data from multiple source systems into consistent schemas
- •Preserve data provenance, timestamps, and traceability required for audits
- •Build reliable pipelines that appropriately handle pagination, API rate limits, retries, schema changes, and partial failures
- •Develop and test features directly within existing internal applications
- •Work within established code patterns, code review processes, automated testing, and CI/CD workflows
- •Improve AI-assisted workflows used for activities such as questionnaire responses and evidence-to-control mapping
- •Build evaluation datasets, scoring methodologies, and regression tests to measure AI output quality
- •Implement structured outputs, guardrails, validation, and fallback processes for AI-generated content
- •Instrument production pipelines and automated workflows with logging, monitoring, alerting, and dashboards
- •Identify broken integrations, stale evidence, and changes in automated output quality before they affect compliance deadlines or customer deliverables
- •Document integration architecture, data models, automated control logic, AI evaluation methodologies, and known limitations
- •Build solutions that can be maintained and extended by the internal team following the engagement
Required Qualifications
- •5+ years of hands-on software engineering, automation engineering, or internal platform engineering experience
- •Experience shipping and supporting production code
- •Strong Python and/or TypeScript development skills
- •Experience building REST and/or GraphQL API integrations
- •Experience working with webhooks and event-driven integrations
- •Understanding of authentication and authorization patterns including OAuth, service accounts, and IAM roles
- •Experience building data pipelines and automated tests
- •Hands-on experience with cloud platform APIs and IAM, preferably AWS
- •Experience programmatically querying infrastructure or system state
- •Experience integrating with SaaS, identity, ticketing, or source-control platforms such as Okta, Entra, Jira, GitHub, or Google Workspace
- •Experience handling API rate limits, pagination, schema changes, retries, and failure recovery
- •Experience with ETL or similar data pipeline development
- •Ability to normalize structured and unstructured data while maintaining traceability to source systems
- •Comfortable contributing to an established codebase and following existing engineering patterns
- •Experience writing automated tests that run within CI environments
- •Production experience integrating LLM capabilities into software applications
- •Experience designing structured LLM outputs
- •Experience creating evaluation datasets, regression checks, or other methods for measuring AI output quality
- •Strong technical documentation and written communication skills
Preferred Qualifications
- •Experience with security, compliance, GRC, IT automation, or internal security platforms
- •Familiarity with SOC 2, ISO 27001, FedRAMP, HIPAA, or similar compliance frameworks
- •Understanding of compliance controls, audit evidence, and evidence traceability
- •Experience with compliance automation platforms or security compliance tooling
- •Familiarity with tools such as Vanta, Drata, Secureframe, OSCAL, Cloud Custodian, OPA/Rego, Prowler, or Steampipe
- •Experience working with commercial LLM APIs
- •Experience with prompt engineering, tool use, structured outputs, and LLM evaluation
- •Experience building internal tools or administrative platforms
- •Familiarity with CI/CD pipelines, containerization, and automated testing frameworks
