AI Enablement Engineer
Confirmed on the employer's own hiring board on Sep 28, 2026. First seen by Alion on Sep 18, 2026.
AI Enablement Engineer
Guardify is a mission-driven technology company building digital evidence solutions that protect vulnerable populations and support justice, healing, and accountability. We work closely with Child Advocacy Centers and public-sector partners to safeguard some of the most sensitive data imaginable, and we move fast, thoughtfully, and with purpose.
We’re a lean, collaborative team that values momentum, clarity, and real-world impact.
The Role
Most engineering jobs are scoped to a product. This one is scoped to the company.
You’ll identify areas across Guardify that are repetitive, slow, or error-prone and build solutions that materially change how our teams work. From Sales and Support to Onboarding, Capture Operations, and Marketing, you’ll build AI agents and automation that take mechanical work off people’s plates.
This is a high-ownership, high-trust role. You’ll be given problems, not tickets, and will shape solutions from idea through production alongside the people who use them.
What You’ll Do
Identify repetitive, slow, or error-prone workflows and quickly ship working solutions.
Build AI agents that take action across systems including HubSpot, Intercom, Linear, and Guardify APIs.
Develop agents that work together, run on schedules, retain context, and proactively surface information.
Own the full lifecycle of your solutions, including evaluations, guardrails, observability, reliability, and cost.
Design with security and sensitive-data boundaries in mind from the beginning.
Create documentation and patterns that allow teams to maintain and extend what you build.
Stay current with emerging AI capabilities and turn promising ideas into working solutions.
Initially focus on opportunities across Sales enablement, Support and Onboarding, Capture operations, and Communications.
Requirements
What We’re Looking For
Strong engineering background with the ability to independently design and ship production solutions.
Production experience building LLM applications, including tool use, orchestration, memory, retrieval, and evaluation.
Experience shipping AI agents that real users depend on.
Comfortable quickly learning unfamiliar business functions and translating their needs into technical solutions.
Strong bias toward shipping small, working solutions and iterating based on results.
Ability to question assumptions and determine when a process should be changed or eliminated rather than automated.
Strong general engineering fundamentals.
Experience with or ability to work within TypeScript, React, Node, Postgres, and AWS. Python-heavy backgrounds are welcome.
Experience connecting tools and systems through APIs and building reliable integrations with logging, retries, and error handling.
Understanding of relational data modeling, access control, and multi-tenant data boundaries.
Familiarity with MCP, agent frameworks, and the evolving AI tooling landscape.
Experience with startups, social enterprises, or other fast-moving teams is a plus.
Comfortable working in a high-ownership environment with loosely defined problems.