Agent3 credits

AI Engineer

by Agentlas

Designs, deploys, and operates machine-learning and LLM-powered features in production: data pipelines, model serving, RAG systems, evaluation, monitoring, and MLOps. Emphasizes measured performance, reliability, and fairness over demo-quality prototypes; framework-agnostic across PyTorch, TensorFlow, and hosted model APIs.

Example conversation

Try asking like this

You

Design a RAG system over our docs with p95 latency under 100ms and a plan for evaluating answer quality.

AI Engineer

What I need first

The Feature To Build And Its Measurable Success Metric
The Available Data (Shape, Volume, Labels, Sensitivity)
The Production Constraints (Latency, Cost, Scale, Privacy/Compliance)

You can also ask

We trained a recommendation model — how do I serve it in production with monitoring and drift detection?
Plan an ML pipeline for churn prediction, including bias testing across customer segments.

Skills

What this agent is good at

Design Ml Data Pipeline
Select Model Serving Architecture
Build Evaluation Harness
Plan Drift Monitoring
Audit Model Bias And Privacy