Key takeaways
- An AI engineer builds products on top of LLMs and foundation models, while an ML engineer trains, deploys and maintains custom models.
- Most companies adding chat, search, summarization or agents to a product need an AI engineer, not an ML engineer.
- Vet AI engineers with a practical task, a system design for an LLM feature, and questions about how they evaluate model output.
- The biggest red flag is a candidate who can demo a prototype but cannot explain how they measure quality, cost and failure cases.
- DevKit's senior engineers interview every AI and ML candidate, and shortlisted CVs usually arrive in 3–5 business days.
How do you hire AI engineers?
To hire AI engineers, first define whether you need someone to build features on existing LLMs or someone to train custom models. Then test candidates on real work: a small practical task, a system design for an LLM feature, and how they evaluate output quality. Seniority should match the risk of the work, not the hype around it.
Job titles in this space are inconsistent. The same person may call themselves an AI engineer, ML engineer, LLM engineer or applied scientist, so read the work history, not the title. A clear brief that names the product problem, the data you have and the models you plan to use will filter candidates faster than any keyword list.
AI engineer vs ML engineer: what is the difference?
An AI engineer builds applications on top of foundation models such as large language models. Their work is LLM-powered features, agents, retrieval-augmented generation (RAG), prompt and tool design, evaluation pipelines and guardrails. They are usually strong software engineers who understand model behavior well.
An ML engineer trains, fine-tunes, deploys and monitors custom models. Their work is data pipelines, feature engineering, model training, serving infrastructure and MLOps. They are the right hire when off-the-shelf models do not fit your data, cost or latency needs.
| AI engineer | ML engineer | |
|---|---|---|
| Main focus | Products built on LLMs and foundation models | Training and running custom models |
| Typical work | LLM apps, agents, RAG, evaluation, guardrails | Data pipelines, training, fine-tuning, MLOps |
| Core skills | Backend engineering, APIs, prompt and tool design, vector search | Statistics, model training, feature engineering, model serving |
| Success measured by | Answer quality, reliability, latency and cost per request | Model accuracy, drift, training and inference performance |
| Hire when | You add AI features to a product using existing models | You need your own models or heavy customization |
Which one do you need: an AI engineer or an ML engineer?
If you are adding a chatbot, document search, summarization, classification or an internal agent, start with an AI engineer. Most of these features can be built with existing models, and the hard part is engineering: retrieval, data access, evaluation and safe behavior in production.
Hire an ML engineer when you have proprietary data that general models handle poorly, strict latency or cost limits, or a need to run models on your own infrastructure. Many mature AI products eventually need both roles, with a data engineer supporting them.
If you are not sure, a short discovery call with an engineer who has done both kinds of work is cheaper than a wrong hire. DevKit can help scope the role before you see any CVs.
What skills should an AI engineer have?
A good AI engineer is a solid software engineer first. The model is one component; the rest is APIs, data, testing, monitoring and cost control. Look for evidence that they have shipped AI features to real users, not only built notebooks or demos.
- Strong backend skills, usually Python or TypeScript, plus API and database design
- Hands-on work with LLM APIs, prompt and tool design, and structured outputs
- RAG experience: chunking, embeddings, vector search and retrieval quality
- Agent and workflow design, including when not to use an agent
- Evaluation: test sets, automated checks, human review and regression tracking
- Guardrails, security and privacy: prompt injection, data leakage, access control
- Production thinking: latency, cost per request, caching, logging and fallbacks
How to vet AI engineers and spot red flags
At DevKit, every AI and ML candidate is interviewed by our senior engineers, not only by recruiters. The approach is practical: we want to see how a candidate thinks about a real problem, not whether they remember definitions.
A typical interview combines three parts. First, a practical task or a walkthrough of code they wrote, such as a retrieval pipeline or an agent with tools. Second, a system design for an LLM feature, for example a support assistant over company documents, covering data flow, retrieval, model choice, cost and failure handling. Third, evaluation thinking: how they would know the feature works, how they would catch regressions and what they would log.
We also check communication and English, because AI work involves many trade-offs that must be explained to product owners. The interviewer's notes, level, time zone, availability and monthly rate are included in every profile you receive.
Watch for these red flags, which suggest a candidate is not ready for production AI work:
- Can show a demo but cannot explain how they measured quality or accuracy
- No answer for hallucinations, prompt injection or sensitive data
- Uses agents or fine-tuning for every problem, even simple ones
- Never thinks about cost per request or latency
- Weak general engineering: no tests, no error handling, no monitoring
- Experience limited to tutorials or a single framework with no understanding of what it does underneath
What seniority of AI engineer fits your budget?
For a first AI feature with unclear requirements, a senior engineer or tech lead usually saves money. They will make the architecture choices that are expensive to undo, such as model provider, retrieval design and evaluation setup.
Once the foundations are in place, a mid-level AI engineer can extend features under that guidance. Junior engineers can help with data preparation, testing and integrations, but should not own AI architecture alone. If budget is tight, one senior engineer is usually a better start than two juniors.
How DevKit helps you hire AI and ML engineers
DevKit is an IT staffing company. We match you with proven AI and ML engineers from our own team and private network: senior people who are hard to find and rarely on job boards. Shortlisted CVs usually arrive in 3–5 business days, and engineers typically start in 2–3 weeks.
You can add one engineer through staff augmentation, build a dedicated team, or hire full-time through tech recruitment. Staff augmentation is billed monthly per engineer, with NDA and full IP transfer as standard, and a free replacement with handover if someone is not a fit.
If you only need a one-off prototype and have no plans to maintain it, a freelancer may be enough. If AI is becoming part of your core product, an in-house hire or a long-term engineer from DevKit is the safer choice.
Talk to us about your hiring needs
Tell us the roles you need. We reply within one business day and send vetted CVs in 3–5 days.
hi@devkit.agency