AI AI Engineer
Builds dependable AI-powered product features with measurable quality, safety, and cost control.
What it takes off your plate
- Design AI-powered features and workflow integrations
- Build retrieval pipelines over company knowledge
- Define evaluation datasets and quality metrics
- Implement guardrails for safe model behavior
- Optimize model selection for cost and latency
- Monitor production AI quality and drift
- Review AI feature changes for safety impact
- Document AI system behavior and limitations
Hand it work like this
Ask in plain words. Add “every Monday” and it becomes a scheduled task.
- Design retrieval flows for a new feature
- Build evaluation sets from real usage samples
- Compare model options on quality and cost
- Tune retrieval relevance for weak queries
- Review guardrail coverage for new capabilities
- Investigate quality regressions after model updates
How an AI AI Engineer works with you
Grounded in your business
It starts from your company profile and the files you attach (16 file types), and remembers standing preferences in a private notebook.
Works on a rhythm
Turn any request into a schedule — up to 25 recurring tasks — and results land in chat and living documents.
You decide how far it goes
Ask for drafts and you get drafts; ask it to act and it acts. Work it starts on its own checks with you before anything destructive. Every reply carries a Transparency trail of the steps it took.
Where it’s strong
- Large language model integration
- Retrieval-augmented generation design
- Model evaluation frameworks
- Vector search and embeddings
- AI safety guardrails
- Latency and cost optimization
- Structured output design
- Model selection and fine-tuning
- AI observability and tracing
- Data pipelines for AI features
- Human-in-the-loop workflow design
- API integration patterns
Tools it often uses
- GitHub
- GitLab
- Jira
- Linear
- Sentry
- PagerDuty
Connect any of 1,000+ connectors and approve which teammate may use each one.
Works well alongside
Data Engineer
Builds and operates reliable data pipelines and warehouse infrastructure that keep analytics and AI workloads fed.
Data & AI →
Machine Learning Engineer
Develops, evaluates, and safely deploys machine learning models that deliver measurable business impact in production.
Data & AI →
AI Research Analyst
Tracks AI capabilities, vendors, and trends, translating them into practical adoption recommendations for the business.
Data & AI →
Questions, answered
What does an AI AI Engineer do?
This teammate designs and builds AI-powered features, retrieval pipelines, and workflow integrations. Use it to move AI capabilities from idea to production with evaluation, guardrails, and cost discipline built in. Typical work includes: design ai-powered features and workflow integrations; build retrieval pipelines over company knowledge; define evaluation datasets and quality metrics.
Will an AI AI Engineer act without my approval?
You decide how far it goes. In chat it does what you ask — ask for drafts and you get drafts; ask it to act and it acts — and email from web chat is drafted for you to send. Work it starts on its own, such as a scheduled report, is checked against its autonomy level: additive in-lane work runs, destructive changes are proposed to you first, and out-of-lane actions never run on their own. Every reply shows its steps.
Which tools can an AI AI Engineer use?
Any of Kuvai’s 1,000+ connectors you connect and assign to it — for example GitHub, GitLab, Jira, Linear. You sign in to each tool through its own OAuth screen and decide which teammate may use it.
How do I set up an AI AI Engineer?
Add a teammate with a name and the job title “AI Engineer”. Kuvai writes its persona, duties and working rules, grounds it in your company context and the files you give it, and it can greet you with a first piece of work to start on. It usually takes a minute or two.
Your AI AI Engineer is one hire away.
Describe the job. Kuvai builds the teammate. You decide how far it goes.