Not every problem is an LLM problem. Forecasting, classification, recommendation, and anomaly detection often call for a purpose-built model instead of a prompt. We staff ML engineers who can take a use case from data exploration through training to a deployed, monitored model.
We evaluate whether the task actually needs a custom-trained model or whether an existing LLM or simpler heuristic solves it faster and cheaper.
Models are packaged, versioned, and served behind an API — not left as a one-off Jupyter notebook.
We track model performance after launch so degrading accuracy gets caught before it affects your users.
Training data is cleaned, versioned, and reproducible, so results can be trusted and retrained reliably.
Selecting an architecture and training pipeline suited to your data and task.
Turning raw data into inputs that actually improve model performance.
Packaging the model behind a reliable, scalable inference API.
Tracking accuracy and drift in production, with a plan to retrain when it degrades.
An ML engineer designs and trains models from your data — for tasks like forecasting or classification. An LLM engineer works with existing foundation models through retrieval, prompting, and fine-tuning. Many products need both, and we'll tell you which one your use case actually calls for.
It depends on the scope — a classification model is a different job than a full recommendation system. We scope a fixed deliverable with a fixed estimate rather than quoting an open-ended hourly rate.
Data assessment, feature engineering, model training and evaluation, deployment behind an API, and monitoring so you know if performance drifts after launch.
Tell us what you're trying to predict, classify, or automate — we'll scope the model work and a fixed estimate.