Hire AI & ML Engineers

Data and feature work comes before any model. A capable engineer spends real time understanding where your data comes from, how it is labelled, what leaks future information into training, and which features are actually available at prediction time. Ask how they handle class imbalance, missing values and data that drifts between training and production - the answers separate practitioners from people who have only followed tutorials.

Model evaluation is the next filter. Look for someone who chooses metrics that match the business cost of a mistake - precision versus recall, calibration, ranking quality - and who builds a held-out test set and a simple baseline before anything complex. For LLM features, that means an evaluation set of real questions, automated checks for grounding and format, and human review of a sample, not judging quality by a handful of demos.

MLOps and production skills complete the picture: versioning data and models, reproducible training, deployment behind an API, and monitoring for drift, latency and cost. Responsible AI belongs in the same conversation - privacy of training data, bias checks across user groups, guardrails against prompt injection and harmful output, and a clear human fallback when the model is unsure.

Not every AI problem needs a large language model. Structured prediction on tabular data - churn, demand forecasting, fraud scoring, pricing - is usually better served by gradient-boosted trees or similar classic models, which are cheaper to run, faster, easier to explain and simpler to monitor.

LLMs earn their place with unstructured language: answering questions over documents, summarising, extracting fields from messy text, classifying support tickets or powering a copilot. Even then, a good engineer will start with a hosted model plus retrieval-augmented generation and prompt engineering, and only recommend fine-tuning or a self-hosted model when evaluation, cost, latency or data-residency requirements justify it. An engineer who can argue both sides of this choice is worth more than one who only knows one toolset.

Early work usually goes on an honest audit: what data exists, its quality, what success means in business terms, and what a simple baseline already achieves. This is often where the most valuable finding appears - sometimes a rules-based approach or a better data pipeline solves most of the problem.

From there the engineer typically builds a first model or LLM feature behind a feature flag, with an evaluation harness and monitoring in place from the start, then iterates on the weakest cases. By the end of this phase you should have a pipeline that can be retrained and redeployed repeatably, rather than a notebook only one person can run.

The most revealing exercise is a design discussion on your own problem: what data they would need, which baseline they would try first, how they would evaluate it, and what could go wrong in production. Follow with a walkthrough of a past project, probing what failed and how they detected it. A short take-home on a small, messy dataset shows data-handling habits quickly.

With PixoBots you interview the engineer yourself before they start, and an NDA is signed before kickoff, so you can discuss your real data and use case during the evaluation rather than a hypothetical one.

AI coding assistants speed up the plumbing of ML work: data-cleaning and feature scripts, training and evaluation boilerplate, API wrappers for model serving, test harnesses, and documentation of pipelines and experiments. They are also useful for drafting candidate prompts and synthetic test questions for LLM evaluation sets.

What the engineer keeps is the judgement: choosing the approach, checking for data leakage, interpreting evaluation results, deciding when a model is good enough to ship and designing the guardrails. Every AI-drafted change is reviewed and run by the engineer, and the models, code and IP belong to you.