Hire AI engineers who ship models into production.
Kadmoon is a US software house in Austin, Texas, and our AI engineers for hire build real AI features: LLM and RAG systems over your own data, ML models for forecasting and classification, anomaly detection, and the data pipelines behind them. Every engineer is on our permanent in-house team, we engineer AI into the architecture rather than bolting it on, and you own the code, models, and infrastructure on delivery.
What our AI engineers build
An AI development team is only useful when the model ends up in production doing real work. Our AI engineers and data and AI practice build the full range:
- LLM and RAG features. Assistants, search, and support that answer from your own documents and data, with retrieval, guardrails, and evaluation so the output stays grounded.
- ML models for forecasting and classification. Demand and revenue forecasting, lead and ticket routing, scoring, and other models tied to a metric you already track.
- Anomaly detection. Catching fraud, outages, and outliers in real time so problems surface before they cost you.
- Data pipelines. The ingestion, cleaning, and feature work that feed every model, built to run reliably, not once in a notebook.
AI engineered into the architecture, not bolted on
A lot of AI work fails because a model gets stapled onto a system as an afterthought, with no evaluation, no cost control, and no plan for when it drifts. We engineer AI into the architecture from day one: clean APIs around each model, evaluation sets that measure quality, monitoring for latency and cost, and a path to retrain or swap models as your data changes. That is the difference between an AI demo and an AI feature you can run.
How to hire and vet AI engineers
After building AI across production systems, these are the criteria we would use to judge any AI development team, including ours:
- Software first. AI engineers who can also ship, test, and deploy software, not just call a model API.
- Evaluation, not vibes. A concrete way to measure output quality and catch regressions before they reach users.
- Data and cost sense. A grip on the data behind the model and on latency and token cost in production.
- Ownership. You receive the repository, models, pipelines, and infrastructure on delivery. No lock-in.
- Cadence. A working demo every two weeks and direct control of the backlog, not a black box.
Engagement options and when it fits
You can add individual AI developers to an existing team, or engage a full in-house AI development team that owns the work end to end. It fits best when you have a concrete problem and data to match: search over your documents, forecasting, classification, or anomaly detection. Browse the solutions we deliver, compare us against a software house, or read more about our data and AI engineering. If the goal is still taking shape, we start with a short discovery engagement before committing a team.
Hiring AI engineers: frequently asked questions
What do AI engineers do?
AI engineers build software that uses machine learning and large language models to do work that plain code cannot, then integrate it into a real production system. That covers LLM and RAG features that answer questions over your own data, ML models for forecasting and classification, anomaly detection, and the data pipelines that feed all of it. A good AI engineer is a software engineer first, so the model ships behind clean APIs, tests, and monitoring rather than living in a notebook.
How do I hire AI engineers or an AI development team?
You can hire individual AI developers to add to your team, or engage a full in-house AI development team that owns a piece of work end to end. With Kadmoon you get a senior team that handles discovery, model selection, data pipelines, evaluation, and deployment, with a working demo every two weeks. Confirm who writes the code, ask to see how they evaluate model quality, and require that you receive the repository, models, and infrastructure on delivery.
How do you vet AI engineers?
Look past the model names on a resume and check the engineering underneath. Strong AI engineers can explain how they measure output quality (evaluation sets, not vibes), how they handle hallucination and edge cases, how they keep costs and latency in check, and how the model gets retrained or updated after launch. Ask them to walk through a real system they built end to end. If someone can only talk about calling an API and cannot talk about data, evaluation, and production behavior, that is a warning sign.
What is the difference between AI engineers and data scientists?
Data scientists focus on analysis and building models to answer a question. AI engineers focus on shipping those models as reliable software: APIs, pipelines, evaluation, monitoring, and cost control in production. The two overlap, and on smaller teams one person may do both, but if your goal is a working AI feature in your product, an AI engineer is who takes it the last mile.
When should a company hire AI engineers?
Hire AI engineers when you have a concrete problem where a model earns its keep: search and support over your own documents, forecasting demand, classifying or routing incoming work, or catching anomalies in real time. It fits best when you already have data and a clear metric to improve. If the goal is vague or the data is not there yet, start with a short discovery engagement before committing a full team.
Ready to hire AI engineers?
Tell us about your project and get a technical proposal within one business day.