An AI engineer builds production software around machine learning models rather than researching new ones. The role covers integrating large language models through provider APIs, building retrieval pipelines over your own data, designing agent workflows that call tools and take actions, and running all of it reliably under real traffic and real cost limits. It sits between software engineering and applied machine learning, closer to the software side.
That distinction matters when you hire AI engineers. A research-heavy profile will train you a model you may not need. A generalist backend developer will wire up an API call and stop before evaluation, cost control and failure handling, which is where most AI features actually break.
These are the roles clients hire AI engineers for most often. Every one of them comes from our AI development and generative AI integration practice, with a solution architect and AI tech lead behind the team rather than a lone contractor.
Related: AI Development Services · Generative AI Integration Services · Offshore AI Developers
Large and small models were used together deliberately, the larger one where reasoning quality mattered and the smaller one where it did not, which is how the running cost stayed inside what an MVP could justify. Read the full case study →
Our engineers use AI coding assistants here the way they use a linter, applied to implementation, test generation and review turnaround. Money movement sets the standard, so financial-safety discipline governs every change regardless of how it was drafted. Read the full case study →
One clarification worth making, because it is the part buyers should interrogate: the risk model itself is Oscilar's, a third-party decisioning platform. Our engineers built the integration, the scoring pipeline, the data-quality layer and the explainability tracing around it. Underwriting decisions remained with the client's rules and their compliance team. Read the full case study →
A good partner to hire AI engineers from proves three things before you sign: that the engineers are senior and screened, that you will own the models and training data you pay for, and that you can test the team on real work first. Saigon Technology publishes all three.Â
Senior Engineers Paired With Applied AI, at $26-$46/hr
Our published rate for senior-led implementation runs $26 to $46 per hour, oversight included. Set that against the $140 to $280 band the market charges for senior and lead AI profiles. The gap comes from a cost base outside the US and Europe, and senior engineers working with AI assistance rather than juniors working in volume. Fewer people deliver the same scope with less rework.
It matters on AI work because at premium rates, evaluation harnesses, guardrails and cost instrumentation are the first line items cut, and those decide whether a feature still runs a year later.Â
You Own the Models, the Weights and the Training Data
Contracts assign you the source code, the fine-tuned model weights, the embeddings and the training data, and they state what happens to all of it when the engagement ends. This is the question worth putting to every provider you shortlist, because it is rarely answered on a website.
"Most teams ask who owns the code. The question that matters later is who owns the fine-tuned weights and the training data, because that is what you cannot rebuild from a repository." - Phong Le, AI Tech Lead, Saigon Technology
Three Independent Screening Rounds Before You See a CV
Every engineer clears three separate assessments: Talent Acquisition, a Tech Lead, and HR. They test engineering depth, English proficiency, logical reasoning and teamwork. We publish the mechanism rather than a pass rate, because a percentage is easy to assert and impossible for you to verify.
That matters more than usual on AI hiring, where the field rewards people who can describe a model architecture over people who have kept one running. You then interview the shortlist yourself, the check that actually counts.
See the AI Working Before You Commit
Working demos are open for inspection at experiment.saigontechnology.vn: fracture detection, semantic search, OCR and object detection. They are running systems, not slides, so you can judge the engineering before you buy.
ISO 9001 and ISO 27001, Certified by BSI
Both are third-party audited by BSI in the UK rather than self-declared, covering the information-security controls your own auditors will ask about. We are also a Microsoft Gold Partner.
The Track Record Behind the Team
14+ years · 400+ developers · 850+ projects · 350+ clients · 3 development centres · 30+ AI engineers · 100+ AI projects · 4 dedicated AI teams, and #10 in the Medium category of Southeast Asia Best Workplaces™ in Technology 2026 (Great Place To Work).
On the case studies above we published the engagement, architecture and stack, and left the outcome figures pending. We would rather show work you can verify than a number we cannot source.
A Shortlist in 1–2 Working Days, Then a Two-Week Trial
Send the role and get matched profiles inside two working days. Interview them, then run a two-week risk-free trial before any longer commitment. Engagement models cover dedicated team, project-based, fixed-price, dedicated development centre and build-operate-transfer.
That need production engineering behind a working idea, and need it before the runway argument gets uncomfortable.
Adding an AI feature to software that already has users, where the integration has to respect an existing architecture and an existing on-call rota.
Where explainability, audit trails and data residency decide the design before anyone writes code.
Usually after a freelance or marketplace hire produced a demo that could not be deployed. Clients hiring AI engineers in the USA and Europe come to us most often at this point.
Most AI projects do not fail at the model. They fail at everything around it, and that is what an engineering partner is for. The AI engineers for hire through one should own that surrounding work, not just the model call.Â
Evaluation you can trust
An eval harness scores model output against cases you define, so a prompt change or a provider upgrade produces evidence instead of an argument. Without one, quality is a matter of opinion and regressions ship silently.Â
Drift caught before your users find it
Model behaviour moves as providers update and as your data changes. Monitoring output quality on a schedule turns model drift from an incident into a maintenance task.
Inference cost you can forecast
Token spend scales with usage in ways that surprise finance teams. Routing simple calls to smaller models, caching aggressively and instrumenting spend per feature keeps unit economics intact as volume grows.
Guardrails that hold under pressure
Injection defence, output validation, PII filtering and human checkpoints on consequential actions. These get retrofitted after an incident far more often than they get designed in.
Senior architecture from the first conversation
Including an honest answer when a simpler system beats a model, or when the data is not ready to support what you are asking for. Not every problem is an AI problem, and saying so early is cheaper for everyone.
A senior engineer and a solution architect work through your use case, data, constraints and success criteria. You get an architecture direction and an honest read on feasibility.Â
We define the profile and run the three assessment rounds against it.
Matched candidate profiles reach you inside two working days of an agreed brief.
Technical fit and team fit are yours to judge, before anyone is onboarded.
Real work, your repositories, your process. Continue only if it is working.
A tech lead and solution architect stay on the engagement, with transparent progress through your own tools and CI pipelines.
Grow the team, hold it steady, or transfer engineers in-house under a build-operate-transfer arrangement. We have completed that transfer before, including a six-year engagement that scaled from 2 to roughly 50 engineers and handed over completely.
Saigon Technology's published rate for senior-led AI implementation is $26 to $46 per hour, with architect and tech-lead oversight included. The wider market charges roughly $60 to $90 per hour at junior level and $140 to $280 for senior and lead profiles. Monthly cost per full-time engineer typically lands between $3,200 and $10,500 depending on seniority and specialization.Â
You receive a shortlist of matched candidate profiles within 1 to 2 working days of an agreed brief. After that the pace is set by your interviews. Most clients complete interviews and begin a two-week risk-free trial inside the first fortnight, with full delivery starting once the trial confirms the fit.
Hire in-house when AI is your core product and you can absorb a three to six month search plus ongoing salary. Use a partner when you need production capability this quarter, when the work is a defined build rather than a permanent function, which is the case most teams outsourcing AI development are in, or when you want to prove the business case before creating headcount. Many clients do both, then transfer engineers in-house later.
You do. Contracts assign the source code, fine-tuned weights, embeddings and training data to you, and specify what happens to each at the end of the engagement. Put the same question to every provider you shortlist, specifically about weights and training data rather than code alone.
Three ways, in order of usefulness. Inspect our working demos at experiment.saigontechnology.vn. Interview the shortlisted engineers directly on your own technical questions. Then run the two-week risk-free trial on real work in your own repositories, which tells you more than any assessment we could run on your behalf.
For applied work, prioritize production engineering over research credentials: model integration and provider abstraction, retrieval design, evaluation methodology, prompt engineering, inference cost control, and guardrails against injection and unsafe output. Deep learning and natural language processing fundamentals matter, but a candidate who has never shipped an AI feature to real users is a research hire rather than an engineering one.
Five. A dedicated team working only on your project, project-based delivery for a defined scope, fixed price for well-specified builds, a dedicated development centre for long-term capacity, and build-operate-transfer where the team ultimately becomes yours. All start with the same two-week risk-free trial. If you need to extend an existing in-house team rather than stand up a new one, that is staff augmentation, which has its own page.
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Mar 19, 2026 
