Artificial Intelligence Engineer

Reports to: General Manager

Team: 5 of 10 engineers on the Jemm Arc product (1 Artificial Intelligence Engineer)
Workstation: Jemm Tec MacBook (Provided to you)
Compensation: Competitive; specifics shared at first interview

About Jemm Tec

Jemm Tec LLC builds the Jemm Arc product across hardware and software. We run on private macOS servers and operate a 7-layer platform stack. Our infrastructure of record lives in Jira, Confluence, and GitLab.

The role

We are hiring an Artificial Intelligence Engineer. Along with training our custom LLM. You will use Claude Code or DeepSeek V4+, run primarily inside Cursor IDE.

You will own two components of the Jemm Arc product:

- JAR-5: AI and LLM Training: dataset curation, fine-tune runs, evaluation harnesses, model registry hygiene thereof

- JAR-6: Inference Infrastructure: model serving, latency monitoring, deployment automation thereof

You will also contribute hardware tasks supporting the Jemm Arc product.

What you will do day to day

Three checks at the start of a given working day apart from your main project operations (per Operations Manual §2):

1. Main project: Review and proceed with your main project operations on a daily basis.

2. Jira check: review the dashboard for tasks newly assigned by the General Manager

3. Slack check: scan #engineering for the async standup thread, then scan the #feed_ channels for bot activity thereof

The daily review remains your responsibility at all times; the AI tooling may summarize either feed on request.

Representative work you will drive on demand:

-Building/training the supporting LLM software for the Jemm Arc product

- "How am I doing on my main project today?" & "Is there anything on Jira I can check out?"

- Summarizing the last training run from the AI Training component and Drive eval results

- Tracking status for Jemm Arc Rev 4 across Drive and supplier notes

- Building n8n workflows; the JSON gets posted to #claude_proposals for review before import

How we work with AI tooling

Read first, propose, then act. The AI agents we use shall survey current state before proposing any action for security purposes.

Default deny on writes. Every platform modifying tool call shall route through our workflows for visibility. The agent posts a proposal to #claude_proposals via the n8n webhook, then waits for Approve or Deny.

Standing pre-approvals. The company has pre approved the following without per-action approval:

- Posting summaries to #engineering from the AI Engineer account

- Creating sub-tasks under Jira epics the AI Engineer owns

- Updating Jira tickets assigned to the AI Engineer

- Drafting Confluence pages under the AI Engineer name (publish shall still require a human click thereof)

Working norms across the company

- Slack: #engineering for technical discussion; #help_it for blockers; #incidents only for severity 1 or 2 thereof

- Jira: every task gets a ticket; no work happens without one

- Confluence: reference docs live here once stable; working drafts stay in Google Docs (Manual §7 document type rule thereof)

- GitLab: branch names include the Jira ticket key (e.g., JAR-123-add-cache); MR descriptions reference the ticket

- Drive: working files in "Engineering"; design assets in "Design"; finalized references migrate to Confluence.

What we are looking for

- Comfort owning model training pipelines end to end: dataset curation through evaluation harnesses and model registry hygiene

- Production inference experience: serving, latency monitoring, deployment automation

- Fluent steering of AI co-pilots (Claude Code inside Cursor, DeepSeek v4 and our custom model) while keeping engineering judgment in the driver seat

- Hardware adjacent curiosity; willingness to support Jemm Arc hardware tasks alongside the AI workload

- Strong written communication; you will draft Confluence pages, Jira tickets, and proposal messages daily thereof

- Comfortable operating inside a default-deny approval gate; you treat the gate as a feature, not friction

What the Candidate should have

-Prior experience building software or at least one website available to show us as proof of prior experience

- Prior experience with n8n or a comparable workflow automation platform

- MCP server experience, or a track record of picking up a new agent surface quickly

- Background working in audit-sensitive environments

Pay: From $120,000.00 per year

Benefits:

• 401(k)
• Dental insurance
• Flexible schedule
• Health insurance

Work Location: In person