Marketing AI Engineer

SysAid's marketing team runs on AI. Agents produce content, execute campaign workflows, repurpose assets, and distribute at scale - marketers own strategy, judgment, and quality. AI agents handle content production at scale. Human marketers own direction, judgment, and quality. 

The infrastructure connecting those two needs a technical owner. Someone who can build the foundation that every agent draws from, wire the workflows that make campaigns run automatically, and translate marketing context into machine-readable architecture that actually works in production. If you're a builder who gets genuinely excited by problems that don't have a playbook yet, read on.

About the role

SysAid's Marketing OS runs on three layers: a Context Lake that holds all messaging and design assets, an Agentic Layer where AI executes production work at scale, and Activation channels where marketing meets the buyer. The first and third layers have human owners. You are the technical owner of the first two.

The Context Lake is a GitHub-based repository where SysAid's messaging system, brand guidelines, persona profiles, competitive positioning, and use cases live as structured, machine-readable files. The quality of every agent's output traces back to the quality of what lives here. You build it, architect it, and maintain it so the team can update a positioning doc, and every downstream agent immediately produces sharper output.

The Agentic Layer is where you build the skills, orchestration files, and workflow architecture that agents use to execute content creation, repurposing, and distribution at scale. A demand gen marketer triggers a campaign workflow without explaining the brand from scratch. The system compounds every time it's used.

You'll report directly to the CMO and work closely with the CTO and the engineering team. This is a cross-functional role with deep technical scope. The people you build for are marketers. The infrastructure you build on is AI.

What you'll do

Context Lake and Agentic Layer

• Build and maintain SysAid's Context Lake on GitHub: the structured, machine-readable repository of messaging, brand guidelines, persona profiles, competitive positioning, and use cases that every agent draws from.
• Design the architecture so that when a marketer updates a file, every downstream agent immediately produces sharper output. Repo structure, file schema, naming conventions, update workflows — all of it.
• Build and maintain the Agentic Layer: the skills, orchestration files, automations, and shared memory structures that power SysAid's content production and campaign workflows.
• Set and enforce the quality bar for both layers. Context files that agents can't reliably read are a production bug. Treat them that way.

Cross-team Agents and Key Workflows

• Build agents and automations that serve the whole marketing team. Demand gen, PMM, content, brand, field, and partner marketing all draw from what you build.
• Design and ship the key campaign workflows: the sequences that let marketers trigger a full production cycle without explaining the brand or the brief from scratch.
• Connect the agentic layer to SysAid's martech stack: CRM, marketing automation, paid media, and distribution tools.
• Own the integration architecture and keep it running reliably in production.

Team Enablement

• Train and enable the marketing team to become genuinely AI-native. That means more than showing them the tools. It means building systems intuitive enough that non-technical marketers can use, update, and improve them independently.
• Translate pain points from across the team into working AI solutions, then hand those solutions back in a way that doesn't require you in the room every time.
• Document the system clearly enough that the team's capabilities compound over time, not just yours.

Infrastructure and Technology

• Stay ahead of what's possible in AI tooling, agentic frameworks, and orchestration platforms. Bring new capabilities into the stack before they become obvious.
• Continuously raise the bar on infrastructure quality: faster workflows, smarter context, cleaner integrations, lower maintenance overhead.
• Feed activation results back into the context lake so the system gets sharper with every campaign that runs.

• 2 years of hands-on experience building AI agents in production: not prototypes, systems that real teams actually used.
• Deep understanding of how agents consume structured context and how repo architecture determines output quality.
• GitHub fluency is used as a context and workflow operating system, not just a code repository.
• Familiarity with LLM frameworks, agentic tooling, and low-code orchestration platforms (n8n, Make, or equivalent).
• Martech stack knowledge across CRM, marketing automation, and campaign tools.
• The ability to translate a marketer's pain point into a working technical solution, and to explain the architecture back to them in plain language.
• A builder mentality. You define the problem, figure out the architecture, and ship something that works.
• Comfort operating without a playbook. You find that energizing, not frustrating.
• A robust sense of humor:)

Why SysAid

SysAid sits at the intersection of two things the market hasn't figured out how to combine yet: enterprise-grade ITSM with agentic AI that actually does the work. Most IT platforms added a chatbot and called it AI. SysAid's AI resolves incidents, fulfills requests, triggers workflows, and deploys automations on its own, no code, no developer, no black box. That's a different category. And the market is still catching up to what that means.

Inside the marketing team, the same philosophy applies. AI handles production at scale. Humans own direction, quality, and judgment. The marketing operating system that makes that possible is midway through being built.

The person stepping into this role builds the foundation it all runs on. The Context Lake. The Agentic Layer. The architecture that connects them. When it works, a marketer updates a file in GitHub, and every downstream agent gets smarter. A campaign workflow runs without a single manual step. The system compounds.

That's the opportunity. A genuinely unsolved problem, a team ready to use what you build, and a product that proves every day that agentic AI can do real work at scale.