Profiles in ~48 hoursVetted shortlist after your brief
Start within a weekAfter a short interview or trial
You own the codeIP assignment and NDA
Scale with 30 days noticeAdd or release engineers
Trusted by founders and enterprises worldwide
About hiring AI engineers
Everything to know before you hire AI talent
When you hire AI engineers, you are buying judgment as much as code: which model to call, how to ground it in your data, and how to prove it works. Speqto places engineers who build LLM applications, retrieval pipelines and agents for founders, CTOs and product heads who need production software, not demos.
An AI engineer sits between software engineering and data science. Most of the week goes into the unglamorous parts of an AI product: shaping prompts and tool schemas, building retrieval over your documents, wiring model calls into queues and APIs, and writing the evaluation sets that tell you whether last Tuesday's change made answers better or worse. Treat the job title as a signal of scope: the person who tunes a retrieval query often also owns the deployment, the dashboards and the handover notes.
They also make cost and latency decisions that decide whether a feature is viable: caching, smaller models for easy requests, streaming responses, and fallbacks when a provider is slow. If a candidate only talks about prompts, they are an enthusiast. If they also talk about tests, traces and budgets, they are an engineer. We also ask about failure stories, because engineers who have watched a model quietly degrade in production design better monitoring than those who have only built demos.
Skills we screen for before we show you a profile
We look for solid backend fundamentals first, usually Python or TypeScript, plus hands-on work with at least one model provider and one open-weight model. Candidates should explain chunking strategies, embedding choice, hybrid search and re-ranking, and know when fine-tuning is the wrong answer. Experience matters less than evidence, so we ask for repositories, design write-ups or live walkthroughs of systems they shipped, and compare them with the claims on the CV.
Then we test for the habits that prevent expensive mistakes: building a golden test set before changing a prompt, logging every model call, handling prompt injection and personal data, and degrading gracefully when a model returns nonsense. Profiles reach you within about two business days of your brief. Anyone who cannot explain why an answer was wrong, and how they would find out, does not make the shortlist.
How the engagement works
You choose a single engineer, a small pod with a lead, or a mixed team that adds backend, front end and QA around the AI work. Engineers join your repositories, tickets and standups, and we run weekly demos so you see working software rather than status slides. The first sprint usually pins down the success measure, the data you can safely use and the first evaluation set, which keeps later debates about quality grounded in numbers.
Scaling needs 30 days notice either way. NDAs are available on request and all code and prompts are assigned to you in full. Engineers also use AI coding assistants in their own workflow, which shortens boilerplate and test writing, while human review stays mandatory on anything that reaches production. That mix lets you judge output quality quickly without giving up the review habits that keep AI-written code safe.
Why teams hire from Speqto
A bench, not a single freelancer
If an engineer is unavailable or the fit is wrong, a replacement comes from the same bench, with knowledge handed over.
Chat, copilots and document tools built on hosted or open-weight models, with streaming, caching and graceful fallbacks designed in from the start.
RAG and search pipelines
Ingestion, chunking, embeddings, hybrid retrieval and re-ranking so answers cite your own content and can be traced back to sources.
Agents and tool use
Agents that call internal APIs, plan multi-step tasks and pause for human approval where a wrong action would be costly.
Evaluation and testing
Golden datasets, automated regression runs and human review loops so prompt or model changes are measured instead of guessed.
Model cost and latency tuning
Routing easy requests to smaller models, caching repeated calls and trimming context to bring per-request cost and response time down.
Safety and data handling
Prompt injection defenses, PII redaction, access control on retrieved documents and audit logs suitable for security reviews. We document each control for your reviewers.
Skill set
Skills and tools your AI hire brings
Matched against your stack during screening, not listed for decoration.
Python
TypeScript
OPOpenAI API
ANAnthropic API
LangChain
LlamaIndex
pgvector
Pinecone
Hugging Face
VLvLLM
FastAPI
LALangSmith
How we vet
Five filters before you see a profile
You only interview engineers who have already cleared code review, a live problem-solving session and a communication check.
1Role brief matched to our bench
2Code and project review
3Technical assessment
4Live problem solving
5Communication and culture check
How to hire
Hire AI talent in four steps
A short, low-risk path from your brief to a working engineer. If the first engineer is not right, we replace them.
1
Share your brief
Tell us the role, stack, hours overlap and how long you need the engineer. A 20-minute call is usually enough.
2
Get vetted profiles
In about two business days you receive candidates with assessment notes, code samples and availability.
3
Interview and trial
Speak to the engineers yourself. Start with a short paid trial and continue only if the fit is right.
4
Onboard and ship
Your engineer joins stand-ups and repositories, demos weekly and is backed by a delivery lead.
Included
Every engineer comes with
No hidden extras: the basics of a safe, managed engagement are part of the package.
NDA and IP assignment
Signed before details are shared; everything built is yours.
Weekly progress reports
Plain-language updates and a demo of working software.
Replacement from our bench
If the fit is wrong, a new engineer joins with a proper handover.
Your tools and repositories
Your Git, tickets and chat; we adapt to your process.
Equipment and payroll handled
Hardware, leave and compliance stay with Speqto.
30 days notice to scale
Add engineers or release them as your roadmap changes.
Compare options
Speqto, freelancer or in-house hire
Speqto dedicated engineer
Freelancer
In-house hire
Time to start
About one week
Days, but quality varies
Often two to four months
Vetting
Practical assessment and senior review
You test every candidate
You run the whole process
If someone leaves
Replacement from our bench
You start the search again
Gap until the next hire
Management
Delivery lead plus your direction
Entirely yours
Entirely yours
Scaling
Add or release with 30 days notice
One person at a time
Slow and costly to reverse
IP and NDA
Assigned to you by contract
Depends on the agreement
Standard employment terms
Equipment and payroll
Handled by Speqto
Their own
Yours
FAQ
Hire AI Engineers FAQs
Straight answers on cost, timelines, IP ownership, team setup and how we work. Cannot find yours? Ask a solutions architect directly.
What is the difference between an AI engineer and a machine learning engineer?
An ML engineer usually trains and deploys custom models on your data. An AI engineer mostly builds products on top of existing models, handling retrieval, tool use, evaluation and integration. Many projects need both, and we can staff either or a pair.
Can I hire AI engineers for a single proof of concept?
Yes. Many clients start with one engineer for a short proof of concept with defined success measures, then keep the same person or grow into a pod. Scaling a team up or down needs 30 days notice.
How do you prevent hallucinations in a production AI app?
You cannot remove them entirely. Engineers reduce them with retrieval grounding, source citations, constrained outputs, confidence thresholds and human review for high-risk answers, then measure the remaining error rate with an evaluation set. Expect residual error, so critical answers keep a human check.
Who owns the prompts, code and fine-tuned models?
You do. Speqto assigns full IP for all code, prompts, datasets prepared for the project and any trained artefacts. We sign an NDA on request before you share anything sensitive. Nothing is held back or licensed separately, and the assignment is written into the contract.
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