DEV Community: TechHub Asia

Stop Using Live Whiteboard Interviews to Hire Remote Developers

TechHub Nguyen — Fri, 07 Aug 2026 05:02:34 +0000

If you've been hired for a remote engineering role recently, you've probably run into this problem: the candidate who aced your technical interview turns out to be the one who quietly stalls your sprint two months later. Everyone on the team liked them. Their code review comments looked fine. And yet tickets kept sitting untouched for days, and nobody could quite explain why.

That's not bad luck, and it's not a hiring mistake in the way most people think about hiring mistakes. It's a mismatch between what your interview process measures and what the job actually requires. Most technical interviews are still built around synchronous, in-office work: a candidate solves a problem live on a call, an engineering manager watches and asks follow-up questions, everyone reacts in real time. That format tests a real skill. It's just not the skill that determines whether someone succeeds on a distributed team.

Remote work runs on a different capability almost entirely: asynchronous discipline. That's the ability to keep making progress when nobody is available to unblock you, and to communicate your work clearly enough that a teammate twelve time zones away can pick it up without a live conversation. A developer can be excellent at solving problems on the spot and still be weak at this. Interviews rarely separate the two, which is exactly why so many technically strong hires underperform once they're actually working remotely.

The two skills, in plain terms

Synchronous technical skill is what you're testing when you put someone in front of a whiteboard or a shared coding environment and watch them think out loud. It tells you how someone performs under observation, with help nearby if they get stuck.

Asynchronous operational skill is something else. It's what determines whether someone can still be productive eight hours into a blocker, with no manager online, no Slack response coming anytime soon, and a decision to make about what to do next. Almost no standard interview process measures this directly, even though it's the actual daily condition of most remote jobs.

When a live coding round still makes sense

If the role genuinely involves real-time collaboration, pairing sessions, or fast incident response with a co-located team, testing raw problem-solving speed under observation is reasonable, because that's close to the real working condition.

For most distributed roles, though, this format tells you almost nothing about how someone behaves when no one is watching, which is the situation they'll actually be in most of the time.

The better test: a specific take-home, not an open-ended one

Give the candidate a broken repository and one clearly defined bug to fix. Set a reasonable deadline, and then don't hover. What you're actually evaluating isn't whether they fix the bug — most competent developers will. What you're evaluating is how they submit it.

This is the part almost every hiring process skips, and it's the part that matters most. When you review the take-home, don't stop at whether the code works. Read the pull request the way a remote teammate would have to read it, with zero context and no ability to ask a quick clarifying question. Did they document their reproduction steps, or did they just submit a diff and assume you'd figure it out? Did they explain why they chose their specific approach, or leave you to reverse-engineer their reasoning? Would someone in a different time zone understand the fix well enough to build on it without a follow-up call?

A developer who writes clean code but can't communicate technical context in writing will block your pipeline constantly. It usually takes months to notice, because the problem looks like a communication issue rather than a hiring issue.

The one interview question that predicts more than the rest of the interview combined

Put the candidate in a specific scenario: they're working on a feature, they hit a blocker, and the only person who can unblock them is asleep for the next eight hours. What do they actually do?

Most candidates describe some version of waiting, checking back later, or sending a message and moving on to something unrelated. The answer that actually predicts remote success looks different. It sounds like documenting the blocker with what's already been tried, pushing their current state so the work isn't lost, and immediately picking up a different ticket from the backlog rather than sitting idle.

That one question filters for self-direction and output continuity faster than almost anything else you can put in a technical interview.

The decision in one question

Will this person regularly need to keep working without anyone available to unblock them?

If the honest answer is yes, and for most distributed roles it is, testing asynchronous skills directly through a take-home, a PR review, and a blocker-handling question matters as much as testing technical ability, maybe more. If the role is genuinely synchronous, a live technical round is still a reasonable choice. And if you're not sure which category the role falls into, it's worth assuming the async version, because most "occasionally remote" roles turn out to be async-heavy in practice once the team is actually distributed across time zones.

None of this replaces technical evaluation. A developer still needs to write good code. But technical skill alone doesn't predict whether someone will thrive on a distributed team, and treating it as the whole evaluation is why so many companies keep hiring well on paper and struggling in practice.

For reference: our full hiring guide

We put together a more complete breakdown of this, covering the full evaluation framework, current salary benchmarks for remote engineers by region and tech stack, and the onboarding steps that keep new hires from losing their first two weeks to access delays and permission requests. If you're actively rebuilding your remote hiring process, it's worth a read before your next round of interviews.

Full guide here: Link

Over to the DEV community

If you've conducted or been through remote technical interviews yourself: what's one question or exercise that actually predicted how someone performed on the job, versus one that looked impressive in the interview but told you nothing useful?

And for anyone who's hired the wrong person despite a strong technical round — what do you think the process failed to test for?

Data Engineer vs. Data Scientist: Which One Does Your Business Actually Need?

TechHub Nguyen — Wed, 22 Jul 2026 07:05:22 +0000

If you've ever tried to hire for a "data" role, you've probably run into this problem: the job title tells you almost nothing.

"Data engineer", "data scientist”, "data analyst with engineering skills", etc. — these titles get used interchangeably on job boards and staffing proposals. But they describe fundamentally different skill sets, solving fundamentally different problems. Hiring the wrong one doesn't just mean a bad hire — it means months spent building the wrong layer of your data stack while the actual problem stays unsolved.

Demand for data engineers specifically is growing about 38% year over year right now, more than 3x faster than the overall IT job market. That growth is exactly why the confusion matters more this year than last: more roles are open, more titles are being used loosely, and more companies are hiring for the wrong one.

Here's how to think about the 2 roles that cause the most confusion — and how to know which one your use case actually requires.

Definition of Data Engineer vs. Data Scientist

Data Engineer: A data engineer builds and maintains the infrastructure that moves, cleans, and stores data — the plumbing everything else depends on. Their work is systems-oriented: pipelines, distributed processing, cloud architecture, data quality, and governance.

Classic use cases: building ETL/ELT pipelines, migrating to a cloud data warehouse, real-time streaming infrastructure, building data lakes that feed everything downstream.
Core stack: Python, SQL, Apache Spark, Kafka, AWS/GCP/Azure, Snowflake or BigQuery, orchestration tools like Airflow.
What they are NOT: A strong data engineer is not automatically a strong modeler. Building a reliable pipeline that delivers clean data on time is a different skill from deciding what statistical model should run on that data.

Data Scientist: A data scientist analyzes data to uncover insights and build predictive models. Their work is statistical and mathematical at its core: hypothesis testing, feature selection, model training, and translating results into something a business can act on.

Classic use cases: churn prediction, demand forecasting, recommendation engines, A/B test analysis, customer segmentation.
Core stack: Python, R, SQL, TensorFlow or PyTorch, Jupyter, Tableau or similar BI tools.
What they are NOT: A data scientist is not a substitute for the infrastructure work. If your data is inconsistent, undocumented, or scattered across five systems, a data scientist will spend most of their time on cleanup instead of the modeling you hired them for.

When to hire each one

Hire a Data Engineer if:

Your data is scattered, unreliable, or arriving late — the fundamentals aren't there yet
You're standing up new infrastructure: a cloud migration, a real-time pipeline, a data lake
The problem is "can we trust this data exists, is clean, and arrives on time"

Hire a Data Scientist if:

You have reliable data already, and the question is what to predict or explain with it
You need a model, not a pipeline: forecasting, classification, segmentation
The problem is "what will happen" or "why did this happen," not "where does the data live"

The technical differentiator: architecture that survives contact with reality

Between two roles, the signal that separates strong hires from resume-keyword-matchers is the same: have they built something that had to survive production, not just a notebook.

For data engineers specifically, that shows up as ETL vs. ELT trade-off reasoning — can they explain why they'd transform data before loading it into a legacy on-prem system, versus loading raw and transforming inside a cloud warehouse for a different workload? Can they talk through automated checks that catch schema drift or null values before they hit a downstream dashboard, and how they'd lock down access with RBAC for sensitive data?

Ask any candidate — regardless of title — to walk through a system they built that broke in production, and how they found out. If the answer is vague, that's prototype experience, not production experience. That distinction matters more than the tools listed on the resume.

What most job postings miss

Two mistakes show up constantly in hiring for these roles:

Expecting one person to do all the jobs. A single hire covering pipeline architecture, statistical modeling, and metrics governance is really all roles stapled into one job description — and it usually means all of them get done at a mediocre level.
Hiring only for the current stack. Screening candidates purely on whether they've used your exact tools (your specific cloud provider, your specific BI tool) filters out engineers with strong fundamentals who could learn your stack in weeks, in favor of people who might struggle the moment your tooling changes — which, in this space, it will.

For reference: our full hiring guide

TechHub Asia published a comprehensive guide on hiring data engineers in 2026 — covering technical and soft-skill screening, interview design (practical coding rounds, system design interviews), salary benchmarks, and the most common hiring mistakes to avoid.
Full guide: Link

Over to the DEV community:

If you've worked across any of these roles — what's the most common mismatch you've seen? Data scientists hired to do what was actually a pipeline problem? Data engineers expected to own ingestion?

And for those who've done the hiring: what's the one interview question that actually told you whether someone could ship, versus just talk about shipping?

ML Engineer vs. LLM Engineer vs. Agentic AI Developer: Which One Does Your Business Actually Need?

TechHub Nguyen — Mon, 13 Jul 2026 07:54:19 +0000

If you've ever tried to hire an "AI developer," you've probably run into this problem: the job title tells you almost nothing.

"AI engineer," "ML engineer," "LLM developer," "AI specialist," "data scientist with AI experience" — these labels get used interchangeably on resumes, LinkedIn profiles, and staffing proposals. But they describe meaningfully different roles, different technical skill sets, and different types of problems they're equipped to solve.

Hiring the wrong type doesn't just mean a bad hire. It means months of work on the wrong layer of the stack — while the actual problem remains unsolved.

Here's how to think about the three roles that cause the most confusion, and how to know which one your use case actually requires.

The three roles clients confuse most

Machine Learning Engineer
An ML engineer builds systems that learn patterns from data to make predictions or decisions. Their work is statistical and mathematical at its core: feature engineering, model selection, training pipelines, validation, and performance evaluation.

Classic use cases: fraud detection, churn prediction, recommendation engines, demand forecasting, anomaly detection.
Core stack: Python, PyTorch or TensorFlow, scikit-learn, Pandas, SQL, feature stores, experiment tracking (MLflow, Weights & Biases).
What they are NOT: A good ML engineer is not automatically equipped to build an LLM-powered chatbot or a RAG system. Those require a completely different toolset and mental model — prompting, retrieval architecture, context windows, vector databases — that most traditional ML engineers haven't worked with.

LLM / GenAI Engineer

An LLM engineer builds applications powered by large language models — chatbots, AI copilots, document Q&A systems, content generation tools, RAG architectures, and prompt pipelines.

Classic use cases: internal knowledge base chatbots, customer support automation, AI writing assistants, document summarization, semantic search over enterprise data.
Core stack: OpenAI API, Anthropic API, LangChain, LlamaIndex, vector databases (Pinecone, Weaviate, Chroma), RAG architectures, prompt engineering, evaluation frameworks.
What they are NOT: An LLM engineer is not necessarily equipped to train or fine-tune foundation models from scratch — that's ML territory. And an LLM engineer building a chatbot is a different hire from an Agentic AI developer building a system that takes autonomous, multi-step actions.

Agentic AI Developer

An agentic AI developer builds systems where AI takes autonomous, multi-step actions — often using tools, APIs, and external systems to complete tasks without human intervention at each step.

Classic use cases: AI research agents, automated workflow orchestration, multi-agent systems for data collection or analysis, AI that books meetings, submits forms, or coordinates tasks across systems.
Core stack: LangGraph, AutoGen, CrewAI, Model Context Protocol (MCP), function calling, tool use patterns, orchestration frameworks, state management.
What they are NOT: Agentic AI development requires deep understanding of agent failure modes, loop detection, tool reliability, and system safety — skills that don't automatically follow from LLM or ML experience. An LLM engineer who has built chatbots is not automatically ready to build reliable agentic systems.

When to hire each one

Hire an ML Engineer if:

Your use case is prediction, classification, or ranking from structured data
You have a labeled historical dataset to train on
The problem is statistical: "what will happen?" or "which option is better?"
You need a recommendation engine, anomaly detector, or forecasting system

Hire an LLM / GenAI Engineer if:

You're building anything powered by language: chatbots, copilots, document Q&A, summarization
Your primary data source is text — documentation, emails, support tickets, contracts
The problem is retrieval and generation: "find the right information and explain it clearly"
You need semantic search over unstructured data

Hire an Agentic AI Developer if:

You need AI to take autonomous, multi-step actions — not just answer questions
Your use case involves coordinating across multiple tools, APIs, or systems
The AI needs to make decisions, retry on failure, and complete workflows end-to-end
You're building AI agents for research, automation, or task orchestration

The technical differentiator: production experience

Across all three roles, the skill that separates strong hires from weak ones is the same: experience deploying AI to production with real users.

Building a prototype that works in a demo is accessible to any reasonably skilled engineer. Building a system that works reliably at scale — with monitoring, guardrails, evaluation frameworks, and graceful failure handling — requires a different set of instincts.

During technical screening, ask every candidate: "Walk me through an AI system you've deployed to production. What broke after launch, and how did you handle it?"

If they can't answer with specifics — actual failure modes, real debugging processes, concrete monitoring setups — they're prototype-capable, not production-ready. That's a meaningful distinction depending on what phase you're in.

What most AI job postings miss

Most AI job descriptions list technologies. They ask for Python, LangChain, PyTorch, and OpenAI API experience. That's a start, but it screens for exposure, not capability.

The two things most postings miss:

Domain understanding. An AI engineer who has worked in your industry will onboard faster and make better trade-off decisions. A fintech AI engineer understands risk and compliance constraints. A healthcare AI engineer knows PHI handling requirements. Always ask for domain-relevant examples, not just generic AI work.

Business problem translation. The best AI engineers — across all three roles — ask about your process before they ask about your stack. If a candidate goes straight to technical questions without understanding what problem the AI is supposed to solve and for whom, that's a red flag regardless of how strong their GitHub profile is.

For reference: our full hiring guide

TechHub Asia has published a complete guide on how to hire AI developers in 2026 — covering all seven AI specializations, a decision framework for matching use case to role, the AIMS hiring framework, cost benchmarks by region and seniority, and an expanded section on when not to hire an AI developer yet.

Full guide: How to Hire AI Developers — TechHub Asia

Over to the DEV community:

If you've worked on the client side or as a vendor placing AI talent — what's the most common role mismatch you've seen? ML engineers hired for LLM work? Data scientists brought in for what was actually an MLOps problem?

And for those on the engineering side: what's the question you wish clients had asked before they hired you?

Dedicated Developer vs. Staff Augmentation vs. Project Outsourcing: Which Model Does Your Business Actually Need?

TechHub Nguyen — Wed, 08 Jul 2026 08:39:37 +0000

If you've ever tried to hire external engineering help, you've probably run into this problem: the labels mean different things depending on who's selling.

"Dedicated developer," "staff augmentation," "managed development team," "offshore outsourcing" — these terms get used interchangeably in vendor proposals and job boards. But they describe fundamentally different engagement structures. Choosing the wrong model before you understand the difference costs you months of ramp-up time and significant budget before the real problem becomes visible.

Here's how to think about the distinction — and how to know which model your business actually needs right now.

The three models, in plain terms

Project outsourcing: You hand a scoped deliverable to an external team. They own the delivery. You define the output (an app, a feature, a migration), agree on a timeline and price, and receive the result. The vendor manages the engineering process internally. You're buying a product, not a team.

Staff augmentation: You temporarily extend your existing team with external developers. They work inside your sprint cycle, your tools, your codebase — under direction from your own technical lead. It's capacity expansion, not capability transfer. This model requires you to already have engineering management in place.

Dedicated developer: You embed one or more external developers into your organization for an extended period — typically 6 to 12+ months. They work exclusively on your product, under your direction, within your team's workflow. Unlike staff aug, this model doesn't require an existing team — the dedicated developer becomes your engineering function or a core part of it.

Each model answers a different question. Project outsourcing answers "can someone build this thing for us." Staff augmentation answers "can someone extend what our team is already doing." Dedicated developers answer "can someone own an engineering function in our product long-term."

When project outsourcing is the right call

You have a fully specified deliverable with clear acceptance criteria
You don't want to manage the engineering process day-to-day
The work is bounded and unlikely to evolve significantly during delivery
You have internal technical capability to review and accept the output

The risk with outsourcing: scope creep and the handoff gap. What the vendor builds and what you actually needed can diverge — and you often won't know until delivery. The handoff also creates a documentation and knowledge gap that's expensive to close if you later need to maintain or extend what was built.

Project outsourcing works well for defined, time-bound work. It works poorly for products that are still evolving or for teams that need to own what gets built.

When staff augmentation is the right call

You have a functioning engineering team with an established technical lead
You need to fill a specific skill gap or extend bandwidth temporarily
The engagement is tied to a specific project phase (3 to 6 months)
You can absorb the developer into your existing sprint and review process

The risk with staff aug: the knowledge walks out the door when the engagement ends. If the augmented developer owns critical pieces of the codebase without thorough documentation, you're exposed.

Staff augmentation works well for teams that are already shipping and need more hands. It works poorly for organizations without existing engineering leadership to direct the work.

When a dedicated developer is the right call

Your product needs sustained, active development over 6+ months
You don't have an in-house engineering team — or your team is too small to carry the roadmap
You need someone who builds context over time, not just executes tasks
You have (or can designate) a product owner on your side who can set priorities and review output weekly

The risk with dedicated developers: the engagement fails when the client side isn't ready. Without a named product owner, a documented backlog, and structured onboarding, even an excellent developer spends the first quarter building the wrong things.

The dedicated model works well for products in active development with clear direction. It works poorly for teams still figuring out what to build — that's a discovery problem, not a hiring problem.

The decision in one question

Does your product need an ongoing engineering function — or a one-time deliverable?

If it's ongoing: dedicated developer or staff augmentation (choose based on whether you have existing engineering leadership).
If it's one-time and fully specified: project outsourcing.
If you're still figuring out what to build: none of the above — nail down requirements first, then hire.

The technical signal clients miss most often

Whichever model you choose, the most common mistake in vendor evaluation is screening only for technical skills and ignoring two things that matter as much: business logic translation and documentation habits.

The best external developers — dedicated or otherwise — ask about your process before they ask about your stack. "Where does your current workflow break down?" matters more in week one than "what version of Node are you running?"

And documentation isn't an afterthought. For dedicated and staff aug engagements especially, clean documentation is the only thing that preserves value if the engagement ends or the developer rotates.

Ask candidates to show you documentation they've produced on a past project. The answer tells you more than a technical test.

For reference: our full hiring guide

TechHub Asia published a comprehensive guide on the dedicated developer model specifically — covering how to evaluate candidates, what the engagement structure should look like, cost benchmarks by region and seniority, and the onboarding frameworks that consistently shorten ramp-up time.

If you're deciding between models or actively evaluating vendors, it's worth reading before you brief anyone.

Full guide: How to Hire Dedicated Developers — TechHub Asia

Over to the DEV community:

If you've worked on the vendor or developer side of these engagement models — what's the most common misconception clients have when they come in asking for a "dedicated developer"?

And for those who've made the wrong model choice: what was the signal you missed before you signed?

Zoho Developer vs Zoho Consultant: Which One Does Your Business Actually Need?

TechHub Nguyen — Fri, 03 Jul 2026 08:42:44 +0000

If you've ever tried to hire someone for a Zoho implementation, you've probably run into this problem: the job title tells you almost nothing.

"Zoho developer," "Zoho consultant," "Zoho expert" — these get used interchangeably on job boards, Upwork profiles, and LinkedIn. But they describe meaningfully different roles. Hiring the wrong type first costs you time and money before a single module is live.

Here's how to think about the distinction.

The difference, in plain terms

A Zoho consultant focuses on strategy and process design. They answer questions like: Which Zoho products fit your use case? How should your CRM pipeline be structured? What should your approval workflows look like? They're strong on business analysis and platform knowledge — but they may not write a line of code.

A Zoho developer focuses on technical implementation. They configure modules, write Deluge scripts, build applications in Zoho Creator, and connect Zoho to external systems via REST API or Zoho Flow. They turn requirements into working software.

The best Zoho professionals do both. But they're usually dominant in one direction, and knowing which you need changes your entire evaluation process.

When to start with a consultant

You haven't decided which Zoho products apply to your use case
You have requirements but haven't mapped them to platform capabilities
A previous implementation failed and you're diagnosing why
Your internal stakeholders disagree on how processes should work

Starting with a developer before requirements are clearly defined is one of the most common and costly mistakes in Zoho projects. You'll build the right thing — for the wrong process.

When you can go straight to a developer

Requirements are documented and specific
You know exactly which Zoho products are in scope (CRM, Creator, Books, etc.)
The work involves: custom Deluge automations, Creator app builds, API integrations, or Analytics configurations
You have someone internally who can validate what gets built against what was requested

The technical differentiator: Deluge

If you're doing a technical screen, one skill separates configuration-level Zoho work from actual development: Deluge scripting.

Deluge is Zoho's native scripting language. It's used for:

Custom functions and scheduled automations
Conditional logic in Creator apps
Dynamic report generation
Webhook handling and external API calls
Complex approval workflows that the UI can't support natively

A developer who doesn't write Deluge is limited to what the drag-and-drop interface allows. That's fine for basic CRM setups. For anything beyond that — Creator apps, cross-product integrations, custom workflows — Deluge is non-negotiable.

How to test for it during screening: Ask the candidate to share a Deluge function they've written and explain what it does. If they can't produce an example and walk through the logic, they're configuration-level, not development-level.

Other technical signals worth checking

Cross-product experience: Zoho is an ecosystem. Zoho CRM, Creator, Books, Desk, Flow, and Analytics all have different architectures and data models. A developer who's deep in CRM but hasn't touched Creator will hit walls quickly once the scope expands.

API integration experience: Direct REST API calls, Zoho Flow, and third-party middleware (Zapier, Make). If your project requires Zoho to exchange data with QuickBooks, Shopify, Slack, or any external platform, this is critical to screen for.

Data modeling in Creator: Can they design a Creator app schema that scales? Have they worked with large record sets and understood Zoho's storage and query limits? This matters more than most people realize as data volumes grow.

The thing most job postings miss

Most Zoho developer job descriptions focus almost entirely on technical skills. That's a mistake.

The implementations we've seen fail weren't failing because of technical gaps. They failed because the developer built something technically correct but operationally useless — no one on the business side ever got asked how their process actually worked.

The best Zoho developers ask about your pipeline, your reporting gaps, and your team's daily workflows before they open the interface. "Business-first thinking" isn't a soft skill on a Zoho project. It's table stakes.

For reference: our full evaluation framework

We published a comprehensive guide on how to find and evaluate Zoho experts — covering technical screening, pricing models (freelancer vs. certified partner), hidden costs, and where to find vetted talent in 2026.

It's aimed at non-technical business owners, but the technical screening section applies directly to anyone running a hiring process.

Full guide: https://techhub.asia/zoho-developer/

Over to the DEV community:

If you've worked on a Zoho implementation — as the developer, the consultant, or on the business side — what do you wish the other party had understood before the project started?

And from a developer perspective: what's the most common misconception clients have when they hire for Zoho work?