How to Hire an Offshore AI Engineering Team in Vietnam: Roles, Skills and Screening Tasks
Hiring an offshore AI engineering team in Vietnam works only when you name the specific AI engineering role you need instead of a generic "AI engineer," then test each candidate with one screening task that mirrors real delivery work, before a CV, a vendor shortlist or a contract is finalized.
Book a solution review See the offshore development center modelBy Huy Nguyen (David), Founder & CEO · Updated · 10 min read
This guide is for a CTO, VP Engineering, founder or Head of Product adding AI engineering capacity in Vietnam, whether hiring directly or through a vendor. It assumes you have already weighed outsourcing itself, which the outsourcing pillar covers, and are now defining the team through a dedicated development team, an offshore development center or staff augmentation.
In this guide
- What AI engineering role do you actually need?
- The five offshore AI engineering roles, compared
- A screening task for each role, not a generic coding test
- Where AI fits in offshore AI engineering hiring
- What delivery record exists, and what does not
- A sequencing plan: from role definition to signed scope
- Alternatives: who defines roles and runs screening
- Limits of this guide
- Frequently asked questions
- Next step
What AI engineering role do you actually need?
Most offshore hiring pages, including the Vietnam-focused staffing and placement pages that currently rank for this question, sell "AI engineers" and "machine learning engineers" as one broad category, sorted by seniority and rate rather than by what the person actually builds. That category is too wide to screen against: a person who can fine-tune a classification model may never have built a production retraining pipeline, and a person who wires a large language model API into a product may never have trained a model at all. Before writing a job description or sending a brief to a vendor, decide which of five distinct jobs you are hiring for.
The five offshore AI engineering roles, compared
| Role | Core skill | What they actually ship | You need one when |
|---|---|---|---|
| ML / applied AI engineer | Trains, fine-tunes and evaluates a model against a metric | A trained or fine-tuned model with an evaluation report | You are building a prediction, scoring, ranking or generative feature from your own data |
| AI data engineer | Builds and operates the data pipeline a model depends on | Labeled training sets, feature pipelines, data quality checks | Model accuracy is limited by data you cannot yet pipe or label reliably |
| MLOps engineer | Operationalizes a model: versioning, deployment, monitoring, rollback | A release and rollback process, monitoring dashboards, drift alerts | A model already works and must run safely and repeatably in production |
| AI integration / LLM engineer | Wires a model or LLM API into a product: retrieval, prompting, tool calls, guardrails | A working feature: a chat assistant, an agent, a retrieval pipeline | You are building on top of a commercial or open model rather than training your own |
| AI solution architect | Designs the system AI sits inside: data flow, human-approval points, cost and latency budget | An architecture decision record and a build-or-buy recommendation | The scope spans more than one of the roles above, or nobody in the org has done this before |
A single small engagement rarely needs all five. A team of two or three, covering one of the model-facing roles (ML engineer or AI integration engineer) plus an MLOps or data engineer, covers most first AI projects; the solution architect role is often a part-time or fractional function rather than a full hire, at least until the second or third project.
A screening task for each role, not a generic coding test
A generic take-home coding exercise or a whiteboard algorithm question tells you whether someone can program. It does not tell you whether they can do the job above. Each role needs a screening task that produces the same kind of artifact the role ships on a real engagement.
| Role | Screening task | What a strong answer shows |
|---|---|---|
| ML / applied AI engineer | Given a small labeled dataset, train a baseline model, then improve it once, and explain the evaluation metric chosen and why | Whether they pick a metric that matches the business problem, not just the highest accuracy number |
| AI data engineer | Design the schema and pipeline for a stated data source with known quality problems (missing values, duplicates, drift) | Whether they build validation and monitoring into the pipeline, not only the happy path |
| MLOps engineer | Design a rollback plan for a model that starts degrading two weeks after deployment, including what they would monitor | Whether they treat model degradation as an expected operating condition, not an incident |
| AI integration / LLM engineer | Build a small retrieval-augmented feature against a provided document set and explain where it can hallucinate or fail | Whether they design for the failure case, not only the demo case |
| AI solution architect | Given a one-paragraph business problem, propose two candidate architectures and the trade-off between them | Whether they can state a real trade-off (cost, latency, data access, maintenance) rather than defaulting to the newest model |
Score each task against a short rubric tied to the deliverable above, not a single pass/fail judgment call. A rubric survives staff turnover on your own side and gives you something to compare across candidates a vendor shortlists on different days.
Where AI fits in offshore AI engineering hiring
Netbase works with the major commercial and open-source AI tools and models, chosen per project, rather than standardizing on one vendor's stack; this shapes which screening tasks are worth running, since a candidate's familiarity with one specific model or framework matters less than their ability to reason about the trade-offs above. Each item below states how mature it is at Netbase.
-
In delivered work
Retrieval-augmented knowledge assistants, document AI and MLOps pipelines
These engagements used the same role split this guide describes, for clients that are not named; no count, duration or result is published for them.
-
In delivered work
An AI chatbot on WhatsApp Business with CRM integration
Built by an AI integration engineer wiring a hosted language model into intent handling and a two-way CRM sync, for a client that is not named.
-
Available capability
AI-assisted technical screening
Using an AI agent to pre-score a screening task's output against a rubric before a human reviewer looks at it; not yet tied to a published case.
What delivery record exists, and what does not
Netbase's own six-step delivery lifecycle names team assembly and architecture planning as a distinct step, run before agile execution starts, on every engagement regardless of the team's discipline. Since 2020, Netbase has worked as offshore development and managing partner on a multi-tenant cloud ERP for a US client that is not named, assembling and running the team behind it; the same team-assembly and governance practice applies when the roles being assembled are AI engineering roles, as on the anonymised MLOps and WhatsApp AI chatbot engagements above. Client-owned IP, secure code review, role-based access control and NDAs on every contributor apply to an AI engineering hire the same way they apply to any other engineering hire.
What does not exist. No published Netbase record states a time-to-fill, an attrition rate, a screening-task pass rate or a number of AI engineers placed; this guide is a method, not a benchmarked outcome, and no figure is claimed for it.
A sequencing plan: from role definition to signed scope
-
Write the role, not "AI engineer."
Pick one row of the table above (or a short combination) before sourcing starts.
-
Pick the one screening task that matches it
Resist adding a second, unrelated task "to be thorough"; it mostly adds time, not signal.
-
Score against a rubric tied to the deliverable,
not a single interviewer's impression, so a vendor's shortlist and your own interviews can be compared the same way.
-
Run a short paid trial task or trial period
on the specific deliverable the role ships, before a long-term commitment.
-
Carry the screening evidence into the contract
Where the engagement is fixed-price, the trial's output becomes part of what "done" means in the written scope and acceptance criteria, not a separate hiring artifact that is discarded once work starts; fixed-price AI projects covers how to write that scope and acceptance clause itself.
-
Choose the engagement shape last
A dedicated team, an offshore development center or staff augmentation each assume the roles are already defined; deciding the shape first, then trying to fit AI roles into it, is a common cause of a mismatched hire.
Alternatives: who defines roles and runs screening
| Route | Strength | Choose it when |
|---|---|---|
| Your own team | Deepest knowledge of your product and data | Someone in-house has already hired or managed this exact role before |
| A staffing vendor's pre-screening | Fast, large candidate pool | The roles are common and well specified; verify the vendor's screening task matches the table above, not a generic one |
| A consulting delivery partner | One team defines the role, runs the screening and then delivers the work, so the trial task is also real project work | You want the screening evidence to carry forward into a fixed-price, accepted scope, rather than stopping once a hire is made |
In Netbase custom development, the client owns the IP created for the work, and most projects run on fixed-price contracts agreed after discovery, with milestone-based, monthly team retainer and KPI-linked terms also offered where they fit the scope better. The offer sits in dedicated development teams and the offshore development center model, extended with the role and screening practice this guide describes; the AI knowledge assistant solution and professional services industry page cover an adjacent delivery shape.
Limits of this guide
- No price, rate or team size is published; scope and commercial terms are set per contract after discovery.
- No time-to-fill, attrition or pass-rate figure is claimed for the screening tasks above.
- The role taxonomy is a starting point; a real engagement sometimes needs a sixth role (for example, a data scientist focused on experimentation and statistics rather than production systems) that this guide folds into the ML/applied AI engineer row for simplicity.
Plan the next step with a Netbase consultant
Frequently asked questions
No. Most first AI projects need two or three people: one model-facing role (an ML engineer or an AI integration engineer) plus an MLOps or data engineer; the solution architect role is often part-time until later projects.
Sometimes, for the AI integration engineer role, since it is closer to product engineering than to model training; it rarely works for the ML engineer or MLOps engineer roles, which need direct experience evaluating and operating models.
Where possible, yes, for the AI data engineer and ML engineer tasks, since data quality problems are specific to a company's own sources; a generic public dataset is an acceptable substitute when your own data cannot be shared before a contract is signed.
A general checklist covers cost, time zone and contract terms, which the outsourcing pillar and IP protection guide already address; this guide adds the role definition and screening-task layer that is specific to AI engineering work.
Next step
Tell us which of the five roles you are hiring for and we will book a solution review to scope the screening task and the engagement shape together. You can also read how an AI-first offshore development center runs, or browse more Netbase insights.
Work behind this guide
Netbase delivered an MLOps pipeline remotely, combining AI consulting, engineering and integration.
Keep ReadingRelated services and solutions
An AI-augmented dedicated development team in Vietnam that answers for results
Netbase provides dedicated development teams from Vietnam for product companies and enterprises to add engineering capacity without losing control. Teams of 3 to 30 people work only on your product, use AI-assisted engineering under review, report to a named account manager and project manager, typically start within one to two weeks after discovery, and the IP they create is yours.
Learn More
AI knowledge assistant: answers from company knowledge, with sources and access control
An AI knowledge assistant is an internal tool that answers employees' questions from your company's own documents and systems, cites the source behind every answer and shows each person only what they may see. Netbase offers it as a growth capability, designed with retrieval, evaluation and human review from the first pilot, and has delivered one for an unnamed client.
Learn More
Work with Netbase remotely
Every Netbase engagement is delivered remotely, so clients in any country and any industry can work with us; Netbase has worked with clients in 80 countries. Contracts and invoices are issued by Netbase JSC, and payment is made by bank transfer to Netbase JSC's company account or PayPal Business. Talk to the CEO, Huy Nguyen (David), on WhatsApp or by email.
Discuss a project
Netbase JSC helps organizations design, build, modernize, and operate digital products and AI-enabled business systems.project@netbasejsc.com
+84 937 869 689
91 Nguyen Chi Thanh, Dong Da, Hanoi, Vietnam
Get in touch
Tell us what you want to build, modernize, or operate.