LLM and generative AI apps
Chat, assistants, summarization, extraction and agent features built on hosted or open-source models and integrated with your product's data and permissions.
Roles · AI & Automation
Hire AI and machine learning engineers to build LLM features into your product
Engineers who take an AI feature from prototype to production. They measure whether the answers are right, keep costs and response times in check, and watch for problems once real users arrive.
From AI demo to a feature customers rely on
Getting a demo working with an LLM API takes an afternoon. A feature customers can rely on takes much more: the right context from your data, measured accuracy, protection against misuse, sensible costs, and monitoring once it's live. An AI engineer, or a machine learning engineer when the work involves training models, builds that inside your product and alongside your team.
What they do for you
- Design and build LLM features: chat, summarization, extraction, classification and agents
- Build retrieval (RAG): chunking, embeddings, vector search and re-ranking over your data
- Create evaluation sets and automated tests that score accuracy before each release
- Choose models and providers on quality, cost, speed and data terms
- Add guardrails against prompt injection, data leaks and unsafe output
- Train, fine-tune or adapt models when off-the-shelf ones fall short
- Monitor live features for quality, cost and response time, and fix regressions
- Explain to product and leadership what is realistic, and what it will cost to run
What separates a real AI engineer from a demo builder
Tools they work in
PythonTypeScriptOpenAI APIAnthropic APIGeminiHugging FaceLangChainLlamaIndexpgvectorPineconeWeaviatePyTorchscikit-learnMLflowWeights & BiasesLangSmithRagasAWS BedrockAzure OpenAIVertex AIDockerMany more
A good fit if:
- You want AI features inside your own product, beyond internal workflows
- You have a prototype that works in demos but not reliably with real users
- You need someone who can tell you which AI ideas are feasible, and what they'll cost to run
Not the right fit if:
- You want to connect existing tools and automate business processes; see AI Workflow Specialist
- You want AI phone agents; see AI Voice Agent Specialist
- Your work is mainly image or video models; see Computer Vision Engineer
Questions for your AI engineer shortlist
01
"Our chatbot gives confident wrong answers about our product. How would you fix it?"
Strong answer: check what context it retrieves, fix chunking and search, require answers grounded in sources, and build an evaluation set to prove the fix.
02
"How do you know whether a prompt or model change made things better?"
Strong answer: a fixed set of test cases with expected results, automated scoring plus human review, and comparing versions before release.
03
"How would you defend an AI feature against prompt injection?"
Strong answer: treat user and document content as untrusted, separate instructions from data, limit what tools the model can call, filter output, and test with known attacks.
04
"When would you fine-tune a model instead of improving prompts or retrieval?"
Strong answer: only when prompts and retrieval have hit their limit, there's enough quality training data, and the gain justifies the cost of keeping it running.
05
"Walk us through an AI feature you shipped. How did it perform once real users had it?"
Strong answer: real usage, numbers on accuracy, cost and response time, and what broke after launch and how they fixed it.
Signs of a strong AI engineer:
- Builds an evaluation set before tuning anything
- Talks about cost per request and response time unprompted
- Treats security and data handling as part of the feature
- Says no to AI where simpler code would do the job
Signs of a weak AI engineer:
- Judges quality by trying a few prompts by hand
- Can't explain how retrieval works under the framework they use
- Has built demos and notebooks, with nothing in production
RAG and AI search
Retrieval over your documents and data, covering ingestion, chunking, embeddings, hybrid search, re-ranking and citations, so answers are grounded and checkable.
Machine learning engineering
For problems that need trained models: classification, forecasting, recommendations or fine-tuned language models, with data pipelines, experiment tracking and MLOps to deploy and monitor them. This is where a machine learning engineer fits.
How the AI engineer search runs
- 1
Talk it through
We agree the work, the tools, the hours you share and who they report to. For this role: what the feature should do, where your data lives, any compliance limits on providers, and whether you need model training or mainly LLM integration.
- 2
Meet a shortlist and choose
We screen a wide pool for every role and introduce only the few who fit your brief. We source, screen and test candidates against your brief, and run background and reference checks. You interview and decide.
- 3
Start with support in place
They start on the work in the brief, and we stay in contact once they've begun.
This is a specialist search and often runs past the one to three weeks most roles take. We recruit internationally whenever the skills you need are scarce.
How pricing works for an AI engineer
All of our ways to hire are open for an AI engineer, for a single feature or a longer roadmap. The choice is yours, and we can discuss the differences on the call.
Who does what
Swipe to see more →
| Task | You | JiveTalent |
|---|---|---|
| Product decisions, provider choices and data policies | Own | – |
| AI features, evaluation results and monitoring | Review results before each release | Your engineer builds and reports on them; at check-ins we see whether the work meets your expectations |
AI and ML engineer FAQ
What's the difference between an AI engineer and a machine learning engineer?
An AI engineer mostly builds products on top of existing models: LLM features, retrieval and agents. A machine learning engineer also trains and deploys models. Many people do both, and this page covers both.
Will they work during our business hours?
The brief sets the hours and the overlap with your team before the search starts, so every candidate is hired for them.
Which models and providers will they use?
Whatever suits your needs and data rules: OpenAI, Anthropic, Google, open-source models or cloud-hosted versions such as AWS Bedrock and Azure OpenAI. Choosing well is part of the job.
What if it isn't working out?
Talk to your account lead. We go through it with you and agree a way forward.
Can they work with our sensitive data?
Yes, with the right setup: providers with suitable data terms, private deployments where needed, and access limited to what the feature needs. Mention your compliance requirements at the start, so we look for engineers who've worked under similar rules.
Who owns the models, prompts and pipelines?
You do. You own the code, prompts, evaluation sets, data pipelines and any fine-tuned model weights, and they're kept in your repositories and your cloud and AI provider accounts.
Do you hire for research-level ML?
Our focus is applied engineering: shipping AI and ML features in products. If you need research-level work, raise it early so we can tell you plainly whether it's a good fit.
How do we see what our AI engineer is working on?
Their hours are tracked in the client app and reported there for you to see.
Ready to put AI in front of real users?
Book a call. We'll agree the brief for your AI engineer and talk you through the ways to hire.