- Home
- Engineering
- AI Engineering
Hire AI engineers who make LLMs work in production.
TekRecruiter is an AI engineering recruiting agency focused solely on technology and engineering roles at tech and SaaS companies. We screen for engineers who understand the models and infrastructure first, then build the retrieval, evaluation and guardrails around them, so you hire people who turn a promising demo into an AI feature customers trust. Beyond skills and experience, we look for HEARTThe HEART standardHHigh agencyEExecutionAAccountabilityRResourcefulnessTTransparencyWhat we look for, beyond skills →.
Trusted by teams at
What is an AI engineer?
An AI engineer builds software products and systems on top of foundation models: choosing and serving the model, grounding it in company data with retrieval, wiring in tools and agents, and proving it works with evaluations and guardrails. A machine learning engineer, by contrast, trains and deploys custom models on a company’s own data, and a data scientist uses statistics and models to answer business questions.
- AI engineer
- Builds applications on foundation models: retrieval, tool use and agents, evaluations, guardrails, and cost and latency control.
- Machine learning engineer
- Trains, deploys and monitors custom models on a company’s own data, from feature pipelines to model serving.
- Data scientist
- Uses statistics, experiments and models to answer business questions and guide decisions.
What companies hire AI engineers to do.
“Add AI” is not a job description. These are the six mandates behind the AI engineering roles we fill.
01 · Retrieval (RAG)
Answers grounded in your data, not the model’s guess.
Retrieval-augmented generation that finds the right document, passage or record and cites it, so answers stay accurate as the data changes.
- Chunking, embeddings, hybrid search and reranking tuned against real queries
- Permissions enforced at retrieval time, not patched on afterward
- pgvector
- Pinecone
- Elasticsearch
- LlamaIndex
- Cohere Rerank
02 · Evaluation
Know it got better, not just different.
Evaluation sets, automated graders and human review that show whether a prompt, model or retrieval change improved quality before users find out.
- Golden datasets built from real user traffic
- LLM-as-judge scores checked against human labels
- Braintrust
- LangSmith
- Ragas
- promptfoo
- Weights & Biases
03 · Agents
Agents that finish the task, and stop when they should.
Tool-using agents that take multi-step actions in real systems, with limits on what they can touch and a human in the loop where the stakes are high.
- Function calling and structured outputs, validated before they act
- Step limits, retries and approvals for irreversible actions
- LangGraph
- OpenAI Agents SDK
- Model Context Protocol
- Claude
- Temporal
04 · Cost and latency
An AI feature the margin can afford.
Model choice, routing, caching and streaming that keep answers fast and unit costs flat as usage grows.
- Easy requests routed to small models, hard ones to frontier models
- Prompt caching and token budgets set per request
- vLLM
- LiteLLM
- Amazon Bedrock
- Azure OpenAI
- Redis
05 · Fine-tuning
Tune the model only when prompting and retrieval run out.
Fine-tuning for format, tone or a narrow domain task, after prompting and retrieval have been measured and fall short.
- LoRA and other parameter-efficient tuning on curated data
- A before-and-after eval that justifies the training and hosting cost
- PyTorch
- Hugging Face
- LoRA
- Unsloth
- Vertex AI
06 · Guardrails
Safe enough to put in front of customers.
Input and output checks, prompt-injection defenses and data controls that let security and legal sign off on launch.
- PII redaction and policy filters on inputs and outputs
- Red-team tests for prompt injection and jailbreaks before release
- NVIDIA NeMo Guardrails
- Llama Guard
- Guardrails AI
- Langfuse
- OpenTelemetry
What to know before you hire an AI engineer.
AI titles are new and loosely used. These six questions will save you a mis-hire, whoever runs the search.
AI engineer, ML engineer or data scientist?
Start from the problem. If you’re building product features on foundation models, such as search, assistants or agents, you need an AI engineer. If you train your own models on proprietary data for fraud, ranking or forecasting, you need a machine learning engineer. If you need analysis, experiments and answers for the business, you need a data scientist.
What does an AI engineer cost in 2026?
In TekRecruiter’s 2026 placements, principal software engineers building AI and LLM systems averaged a $170,000 base in South Florida, and most roles were bonus-eligible. Data scientists working in Python and machine learning averaged $155,000 in South Florida. New York generally pays well above South Florida for senior engineering roles. See the 2026 Salary & Rate Guide.
How do you tell a real AI engineer from someone who added an API call?
Real AI engineers start with the model and the infrastructure under it: context limits, how the model is served, what a request costs and why it fails. Then they build the systems that make it dependable, such as retrieval, evaluations and guardrails. Someone who wired one API call into an app can demo it, but can’t tell you how they know it works.
Prompting, retrieval or fine-tuning?
Start with prompting. Add retrieval when answers depend on your data or need to stay current. Fine-tune only when format, behavior or latency still fall short and you have clean training data, because every step adds cost to build and maintain. A candidate who reaches for fine-tuning first, before measuring the cheaper options, is showing you how they’ll spend your budget.
Hire AI engineers or an ML consulting firm?
A consulting firm fits a bounded proof of concept, or deciding whether AI fits a problem at all. Once AI is on the product roadmap, hire. The knowledge of your data, your eval sets and your failure cases compounds inside the team, and it leaves with a consultant. Contract-to-hire sits in between: start on a real problem, then keep the engineer who solved it.
How should you interview an AI engineer?
Ask for a system they shipped to real users. Have them show how quality was measured, name a failure the evals caught, and say what a request cost and how they brought it down. A good practical exercise is debugging a bad answer from a retrieval pipeline, which tests judgment far better than trivia about a framework.
Why AI engineering searches go wrong.
AI is the easiest skill to put on a résumé and one of the hardest to verify. These are the misses we see most.
- 01
A demo, not a deployment.
The portfolio is a chatbot on a public dataset. Nothing ever ran against real users.
- 02
No way to measure quality.
Changes shipped on gut feel, with no eval set to say what got better or worse.
- 03
Model work and product work, confused.
The post asked for model training when the job was retrieval and agents, or the reverse.
- 04
Cost discovered in production.
It worked well until the first invoice from the model provider.
- AI and LLM engineering placements in 2026
- 14
- Average base, principal AI and LLM engineers, South Florida
- $170K
Models first. Then everything around them.
The engineers who make AI work understand the model and the infrastructure under it before they build on top. We take every candidate through an AI system they shipped, from the retrieval design to the eval set to the failure that changed it. Before we source, we ask you whether the role is model work or product work, because that changes the whole search. Each candidate also has to meet HEART.
- Model and infrastructure fluency: which models they used, how they were served, and why that choice fit the latency and cost targets.
- Retrieval they designed: how the data was chunked, embedded and ranked, and how they fixed a wrong answer.
- Evaluation they owned: the eval set, how it was built, and a regression it caught before launch.
- Life after launch: guardrails, monitoring, and who got paged when the model misbehaved.
| A generalist tech recruiting firm | TekRecruiter |
|---|---|
| Counts “LLM” and “LangChain” on the résumé | Walks through an AI system the engineer shipped to real users |
| Treats a demo or hackathon project as proof | Asks for the eval set, the failure and the fix |
| Can’t tell AI engineering from ML research | Scopes model work versus product work before sourcing |
| Skips cost and latency | Asks what a request cost and how they brought it down |
| Prices from AI salary headlines | Prices from our own 2026 AI and LLM placements |
The HEART standard
What we look for beyond skills and experience, in every candidate we present.
- High agencyPeople who see what needs to be done and act without waiting to be told.
- ExecutionPeople who turn ideas into results.
- AccountabilityPeople who own the outcome, not just their piece of the work.
- ResourcefulnessPeople who figure things out when the answer isn’t obvious.
- TransparencyPeople who communicate clearly, honestly, and early.
What we screen out.
The patterns that separate an engineer who ships AI from one who has watched the tutorials.
Framework before fundamentals
Knows the LangChain API, but can’t explain context windows, tokenization or why the model made something up.
No eval set
Judged quality by trying a few prompts by hand.
Fine-tuning as the first move
Reached for training before measuring what prompting and retrieval could do.
Never saw the bill
No idea what a request cost or how latency changed with traffic.
Agents without limits
Gave an agent write access to live systems with no step limits, approvals or audit trail.
Prompt injection is news to them
Hasn’t thought about a retrieved document or user message that carries instructions.
Senior, staff and principal AI engineers.
In AI, level shows in who makes the expensive decisions: which model, which approach, and what it may cost.
Senior
Ships AI features end to end.
- Builds the retrieval, tools and prompts for a feature and takes it to production
- Writes and maintains the evals for what they ship
- Handles cost, latency and failure cases without being asked
Staff
Sets how the company builds with AI.
- Decides between prompting, retrieval and fine-tuning, and owns that cost
- Builds the shared eval and guardrail practice other teams adopt
- Chooses models and providers, and plans for switching them
Principal
Owns AI architecture and risk across products.
- Decides what to build, buy or wait on across the product line
- Works with security and legal on data use, safety and compliance
- Advises leadership on where AI changes the product and the margin
In 2026, our principal AI and LLM placements in South Florida averaged a $170K base, most with a bonus. See the 2026 Salary & Rate Guide.
AI engineer vs. ML engineer vs. data scientist.
Pay is TekRecruiter’s 2026 South Florida data. A dash means we don’t have enough placements to publish.
| AI engineerYou are here | Machine learning engineer | Data scientist | |
|---|---|---|---|
| Builds | Products on foundation models: retrieval, agents, guardrails | Custom models trained on company data | Analyses, experiments and predictive models |
| Core question | Does the AI feature work for users, at a cost we can afford? | Is the model accurate, and does it stay accurate in production? | What does the data say, and what should we do? |
| Typical stack | Python or TypeScript, model APIs, vector search, eval tools | Python, PyTorch, feature stores, model serving | Python, SQL, notebooks, statistics libraries |
| Measured by | Eval scores, task success, latency and cost per request | Model metrics, drift and serving performance | Decisions influenced and experiment results |
| 2026 pay, our data | $170K avg (principal) | — | $155K avg |
How to hire AI engineers with us.
Direct hire
For AI on the product roadmap, where knowledge of your data and evals should stay in house. Backed by a 90-day guarantee.
Direct hireContract-to-hire
Put an AI engineer on a real problem first, then convert the one whose system held up. No conversion restrictions.
Contract-to-hireStaff augmentation
A contract AI engineer for a bounded build: a retrieval pilot, an eval harness or an agent proof of concept.
Staff augmentation
AI engineer hiring questions, answered.
What does an AI engineer do?
An AI engineer builds software on top of foundation models such as large language models. The work covers choosing and serving models, retrieval over company data, tool use and agents, evaluations that measure quality, guardrails, and keeping cost and latency under control in production.
What is the difference between an AI engineer and an ML engineer?
An AI engineer builds products on existing foundation models, so the work centers on retrieval, agents, evaluation and guardrails. A machine learning engineer trains, deploys and monitors custom models on a company’s own data, so the work centers on features, training pipelines and model serving. More on the split: machine learning engineer vs. AI engineer.
How much does an AI engineer cost in 2026?
In TekRecruiter’s 2026 placements, principal software engineers building AI and LLM systems averaged a $170,000 base in South Florida, most with a bonus. Data scientists in Python and machine learning averaged $155,000 in South Florida. See the 2026 Salary & Rate Guide.
Should we hire AI engineers or use an ML consulting firm?
Use a consulting firm for a bounded proof of concept or a decision on whether AI fits the problem. Hire AI engineers once AI is on the product roadmap, because knowledge of your data, evals and failure cases compounds inside the team. Contract-to-hire lets you start on a real problem and keep the engineer who solved it.
How do you screen AI engineers for production experience?
We walk each engineer through an AI system they shipped to real users: the models and how they were served, the retrieval design, the eval set and a regression it caught, what a request cost, and what happened after launch. AI tools can answer standard AI interview questions, so we assess the work itself.
What is the difference between an AI engineer and an AI platform engineer?
AI engineers build AI into products and workflows: the retrieval, prompts, agents and evaluations behind a feature. AI platform engineers build the shared infrastructure those engineers and their agents run on: model gateways and serving, retrieval and evaluation infrastructure, GPU capacity, and the permissions and sandboxes that keep agents safe. Most companies add the platform role once more than one team ships AI.
Do you recruit AI engineers in Miami and South Florida?
Yes. Our 2026 AI and LLM placements were all in South Florida: about 60% in Miami and 40% in Palm Beach and Broward. Half were on site and half hybrid, three days in the office. TypeScript and Python were asked for about equally, with some C#/.NET.
Can we hire AI engineers nearshore?
Yes. We place English-speaking engineers in Latin America who work US hours, at about 50% lower cost than a US hire, and we screen them on the same shipped-system walkthrough. See Nearshore.
What is the HEART standard?
HEART is the standard we screen every candidate against, beyond skills: high agency, execution, accountability, resourcefulness and transparency. It describes people who take ownership, turn ideas into results and move the business forward. See the standard.
Last updated .
Roles that sit next to AI engineering.
Let's find the engineers who make your AI work.
Tell us the models, the data and what the AI has to do. You'll talk with our founder, Ron Smith.
