AI & Machine Learning
Hire AI/ML Engineers
Machine learning engineers who put models into production and keep them working, not notebooks that never ship.
Machine learning earns its place when a decision is made too often for people to make it well every time — demand forecasts, anomaly flags on operational data nobody has time to read, ranking work that would otherwise be a spreadsheet. It stops earning its place the moment the model cannot be retrained, monitored or explained to the person whose job it changes.
When hiring, look past framework familiarity. Ask how a candidate designed a holdout set, what baseline they beat and by how much, and what happened to the model six months after launch. Engineers who have owned a model in production talk about drift, feedback loops and rollback. Those who have not talk mostly about architectures.
What these engineers do
- PyTorch 2.x and scikit-learn for training, evaluation and error analysis
- Feature pipelines, drift detection and retraining triggers that actually fire
- Model serving behind FastAPI, ONNX Runtime or Triton within real latency budgets
- Forecasting, classification and anomaly detection on messy operational data
- Honest evaluation - holdout design, leakage checks and baselines worth beating
Delivered AI-first
AI assistance sits in the delivery loop rather than in the model itself. Engineers use it to scaffold experiment harnesses, dataset loaders, evaluation code and the boilerplate around serving and monitoring, which leaves more of their time for problem framing and error analysis. Every training script, metric definition and inference path is still read line by line in review. The measurable effect is throughput per engineer, not fewer reviews.