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Responsibilities
The MissionDashr.ai is an AI-powered security compliance platform built by security practitioners. We're hiring an AI Automation Engineer with one mandate: automate and agentize everything we do — from risk assessment to security monitoring to compliance evidence workflows — so a small expert team delivers like a large one.You do NOT need to be a cybersecurity expert. Our founder supplies the security domain knowledge and requirements; you turn them into working, reliable, documented automations. If you're a strong AI/automation engineer who learns unfamiliar domains fast and asks sharp questions, this role was designed for you.This is a builder role, not a research role. You'll ship working agents and automations against defined acceptance criteria, starting with a paid proof-of-concept engagement.What You'll DoTranslate security processes described by our founder into AI agents and automation pipelines — starting with security monitoring, evidence collection, KPI reporting, and risk-assessment workflowsBuild reliable LLM workflows: prompt pipelines, structured outputs, tool/function calling, retrieval, guardrails, and evaluation harnessesIntegrate with our operational stack via APIs: SentinelOne, Wazuh, NinjaOne, Microsoft 365 / Entra ID, Azure and AWSInstrument everything: logging, error handling, human-in-the-loop checkpoints where accuracy matters (compliance evidence cannot hallucinate)Document each automation so it encodes the domaitable, and hand-off readyExtract requirements like a consultant: ask the questions that turn a messy verbal process description into a precise specificationMust-HavesHave you personally built and shipped a production LLM-powered automation or agent (not a demo or tutorial project)? (Y/N)Do you have 3+ years hands-on Python (or TypeScript) building API integrations and automation pipelines? (Y/N)Have you built automations or integrations against third-party APIs in a production environment? (Y/N)Strongly PreferredSoftware engineering fundamentals: testing, version control, CI, code review discipline — code that survives a second client, not just a demoExperience making LLM outputs verifiable — evals, structured validation, citation of source evidenceAgent frameworks and orchestration patterns (function calling, multi-step tool use, eval-driven development)Azure automation depth (Logic Apps, Functions, Graph API); AWS equivalent welcomeAny exposure to security or IT tooling (SIEM, EDR, RMM) or compliance frameworks (ISO 27001, SOC 2) — helpful, not requiredHow We WorkDeliverable-based contracts with defined acceptance criteria — outcomes, not hoursAsync-first, multi-workstream environment; you manage your own executionDirect collaboration with the founder as your domain expert and requirements sourceContract-to-hire: strong POC delivery converts to a larger ongoing role with an expanding automation roadmapHiring ProcessPredictive Index two-part assessment: behavioral (~10 min, untimed) + cognitive (12 min, timed)Hands-on technical exercise (50% of decision weight)Structured interview (30%)Decision with weeksNote on IP and ScopeAll engagements begin with an IP assignment agreement before any code access is granted. Certain workstreams — including automation of our proprietary penetration-testing methodology — begin only after IP assignment is fully executed. The technical exercise uses neutral scenarios; no proprietary systems or prior security knowledge required.
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