Everyone Wants AI Agents—But Who Should You Hire to Build Them?

Every company seems to want an AI agent.

Some want an agent that answers customer questions. Others want one that researches markets, qualifies sales opportunities, reviews documents, supports developers or completes administrative work.

The idea sounds straightforward: connect an AI model to company data, provide instructions and allow it to complete tasks.

In practice, building a reliable AI agent is rarely a one-person prompting project.

An agent may need to understand a goal, retrieve information, select tools, complete actions, remember context and recognise when human approval is required. It must also operate securely, produce useful results and recover when something goes wrong.

That creates an important hiring question:

Who should your company hire to build it?

The answer depends on what the agent needs to do, which systems it must access and how much risk is involved.

What Is an AI Agent?

An AI agent is a system that uses artificial intelligence to pursue a goal and complete tasks with some degree of independence.

Unlike a basic chatbot that responds to individual questions, an AI agent may:

• Break a goal into smaller tasks
• Search company knowledge
• Use software tools or APIs
• Analyse information
• Make limited decisions
• Complete actions across several systems
• Remember relevant context
• Ask for human approval
• Evaluate whether a task was completed successfully

For example, a customer-support chatbot may only answer questions from a knowledge base.

A customer-support agent could identify the customer, review their account, check an order, issue an approved refund, update the support ticket and send a follow-up message.

That ability to act makes agents useful. It also makes them more complicated and potentially more risky.

Anthropic distinguishes between structured AI workflows and agents that dynamically decide how to use tools and complete tasks. Its guidance recommends starting with the simplest approach that can solve the problem rather than adding unnecessary autonomy. Read Anthropic’s guidance on building effective agents.

Why Is Hiring for AI Agents Difficult?

“AI agent developer” and “AI agent engineer” are relatively new job titles.

Companies may use them to describe very different responsibilities.

One employer may need a software engineer who can connect an existing model to business systems. Another may need an experienced machine-learning engineer who can develop evaluations, improve retrieval and optimise model performance.

A regulated financial company may also require specialists in security, privacy, risk and AI governance.

Hiring the wrong profile can produce an impressive demonstration that never becomes a safe or reliable product.

Before opening a vacancy, define:

• The problem the agent will solve
• The people who will use it
• The systems it must access
• The decisions it may make
• The actions it may complete
• The data it will process
• The level of accuracy required
• The consequences of an incorrect action
• The points where humans must remain involved
• The results expected during the first 6–12 months

These answers determine which people you need.

The Main Roles Needed to Build AI Agents

Not every company needs a large AI department.

However, most production AI-agent projects require several capabilities—even when one experienced person covers more than one area.

1. AI or LLM Engineer

An AI or large language model engineer normally develops the intelligence and behaviour behind the agent.

Their work may include:

• Selecting suitable AI models
• Designing instructions and agent workflows
• Connecting models to tools
• Building retrieval-augmented generation systems
• Managing context and memory
• Improving the quality of responses
• Creating model evaluations
• Reducing hallucinations
• Balancing accuracy, speed and cost
• Testing how the agent behaves in unexpected situations

This person should understand more than prompt writing.

They need to know how language models behave, where they fail and how to build systems that remain useful outside a controlled demonstration.

An LLM engineer may be the right first technical hire when the agent’s main challenge involves reasoning, retrieval, tool use or model evaluation.

2. Software or Backend Engineer

An AI agent becomes valuable when it can work with real business systems.

That requires strong software engineering.

A software or backend engineer may:

• Build APIs and integrations
• Connect the agent to internal systems
• Manage authentication and permissions
• Create approval processes
• Develop queues and background tasks
• Handle failures and retries
• Record actions for auditing
• Improve system performance
• Build reliable user-facing services
• Maintain the application as usage grows

This role is especially important when the agent must interact with systems such as:

• Customer relationship management software
• Support platforms
• Payment systems
• Internal databases
• Email and messaging tools
• Human-resources platforms
• Enterprise resource planning systems
• Development tools

A company may not need a research-focused machine-learning specialist if it is using established AI models.

It may receive more value from an experienced software engineer who understands AI integration and can build reliable production systems.

3. Data Engineer

An agent cannot provide dependable answers if the information behind it is incomplete, outdated or poorly organised.

A data engineer prepares the systems that allow the agent to access relevant information.

Their responsibilities may include:

• Connecting different data sources
• Building data pipelines
• Cleaning and standardising information
• Managing document processing
• Creating data-access controls
• Maintaining knowledge bases
• Monitoring data quality
• Supporting search and retrieval
• Tracking where information came from
• Managing updates and deletion

A data engineer becomes particularly important when the agent must work across several systems or use large amounts of company information.

Without reliable data, even an advanced model can produce weak results.

4. MLOps or AI Platform Engineer

A prototype may work on one computer with a small number of tests.

A production agent must work consistently for real users.

An MLOps or AI platform engineer helps deploy, monitor and maintain the system.

Their work may involve:

• Managing development and production environments
• Deploying models and AI services
• Tracking model and prompt versions
• Monitoring quality, latency and cost
• Managing logs and observability
• Creating automated tests
• Supporting evaluation pipelines
• Investigating failures
• Managing infrastructure
• Scaling the system as usage increases

This role becomes more important when a company operates several agents or when AI is becoming part of a wider technology platform.

The goal is not simply to launch an agent.

The goal is to understand how it behaves after launch.

5. AI Product Manager

A technically capable agent can still fail if it solves the wrong problem.

An AI product manager connects business needs, user experience and technical delivery.

They may be responsible for:

• Identifying valuable use cases
• Defining the agent’s users
• Setting success measures
• Prioritising features
• Deciding where humans remain involved
• Managing expectations
• Coordinating technical and business teams
• Reviewing user feedback
• Measuring adoption and business value
• Planning improvements

AI products contain uncertainty.

A traditional software feature normally follows defined rules. An AI agent may produce different outputs from similar inputs.

The product manager must therefore understand both user needs and the limitations of AI systems.

For companies that have technical resources but no clear use case, an AI product leader may be more urgent than another engineer.

6. AI Evaluation or Quality Specialist

Companies often spend more time building an agent than deciding how its performance will be measured.

An AI evaluation specialist creates structured methods for assessing the system.

Evaluation may examine:

• Accuracy
• Task-completion rate
• Use of correct tools
• Citation quality
• Safety
• Consistency
• Response time
• Cost per task
• Appropriate escalation
• User satisfaction
• Business outcomes

The specialist may create test scenarios, review failed tasks and identify patterns in the agent’s behaviour.

This work may be completed by an AI engineer in a smaller company. Larger or higher-risk projects may need a dedicated evaluation function.

An agent should not be judged only by whether its responses look convincing.

It should be judged by whether it completes the correct task reliably and safely.

7. Security Engineer

Giving an agent access to company tools creates security questions.

What information can it view? Which actions can it complete? Could a user manipulate its instructions? What happens if it selects the wrong tool or exposes confidential data?

A security engineer may help with:

• Identity and access management
• Tool permissions
• Secure API connections
• Data encryption
• Threat modelling
• Prompt-injection protection
• Audit logs
• Incident response
• Testing for unauthorised actions
• Limiting the effect of a failure

Security is particularly important when an agent can access customer records, financial information, source code, employee data or critical business systems.

The more autonomy an agent receives, the more carefully its permissions should be designed.

8. AI Governance, Risk or Compliance Specialist

An AI agent may affect customers, employees or important decisions.

Companies operating in regulated industries or several countries may need someone who understands the legal and governance requirements.

This role may cover:

• AI policies
• Risk classification
• Documentation
• Data privacy
• Human oversight
• Explainability
• Bias and fairness
• Vendor assessment
• Regulatory requirements
• Approval and accountability

Governance should begin before deployment—not after a serious problem appears.

This does not always require a full-time AI governance hire. The work may be shared between legal, compliance, security and technology teams.

However, responsibility must be clear.

9. Domain Expert

An agent designed for healthcare, recruitment, finance, cybersecurity or another specialist area requires relevant industry knowledge.

A technically strong engineer may understand how to build the agent but not how professionals complete the task in practice.

A domain expert can:

• Explain the real workflow
• Identify exceptions
• Define acceptable results
• Review the agent’s decisions
• Create realistic test cases
• Identify harmful mistakes
• Clarify when human judgement is required
• Help users trust and adopt the system

For example, an agent supporting recruiters should be developed with input from experienced recruitment professionals.

The person does not necessarily need to write code. Their knowledge helps the technical team build the right system.

Which AI Agent Role Should You Hire First?

The correct first hire depends on your biggest problem.

Your situation

Likely priority

You have an idea but no clear use case

AI product manager or product-focused AI consultant

You need to create the agent’s reasoning and tool use

AI or LLM engineer

You already use an AI model but need reliable integrations

Software or backend engineer

Your information is spread across several systems

Data engineer

Your prototype works but cannot scale reliably

MLOps or AI platform engineer

The agent will access sensitive systems

Security engineer

The agent may affect regulated or high-risk decisions

AI governance or compliance specialist

The agent produces inconsistent results

AI evaluation specialist

The agent requires specialist industry judgement

Domain expert

Do not begin by copying another company’s AI job description.

Begin with the problem your own company must solve.

What Does a Small Company Need?

A startup may not have the budget to hire every role separately.

Its first AI-agent hire may need to combine:

• AI engineering
• Backend development
• Model evaluation
• Cloud deployment
• Product thinking
• Basic security awareness

This is often called a product-minded AI engineer, applied AI engineer or full-stack AI engineer.

However, companies should be careful with broad titles.

One person may cover several areas, but no candidate is equally strong in everything. Identify which capability is essential and which skills can be supported by existing employees, contractors or advisers.

A small company building its first agent might use:

1. One experienced applied AI or software engineer
2. One product owner with a clear business objective
3. Part-time input from a domain expert
4. Security and legal review before deployment

As the system grows, the company can add data, platform, evaluation and governance specialists.

What Does a Larger Company Need?

A larger company may already have software, data, security and legal teams.

In that situation, the hiring challenge is often coordination.

The company may need:

• A senior AI engineering lead
• Applied AI or LLM engineers
• AI product managers
• MLOps or platform engineers
• Evaluation specialists
• Security and governance professionals
• Change-management and user-training support

Existing employees should have defined responsibilities.

If everyone is involved but nobody owns the outcome, the project can remain trapped between teams.

Assign one accountable leader who can connect the technical system to a measurable business result.

Should You Build, Buy or Customise an AI Agent?

Hiring a permanent team is not always the correct first step.

Companies generally have three options:

Buy an existing AI product

This may be suitable when the task is common and the available product already meets the organisation’s requirements.

Examples may include meeting notes, customer-support assistance or standard document processing.

Customise an existing platform

This may be appropriate when the company needs its own workflows, data connections and controls but does not need to develop the entire system.

Build a custom agent

A custom system may be justified when:

• The workflow creates a competitive advantage
• Existing products cannot support the process
• The agent requires specialist company data
• The organisation needs greater technical control
• Security or regulatory requirements are complex
• The expected business value justifies the investment

The hiring plan should reflect this choice.

Buying a product may require implementation, data and change-management skills. Building one may require a broader engineering and governance team.

Skills to Look for in an AI Agent Engineer

Job titles are inconsistent, so assess evidence rather than labels.

Relevant technical skills may include:

• Python or TypeScript
• Large language model APIs
• Tool and function calling
• Retrieval-augmented generation
• Vector and traditional databases
• API development
• Agent orchestration
• Evaluation frameworks
• Model monitoring
• Cloud infrastructure
• Authentication and permissions
• Data privacy and security

Important non-technical skills include:

• Understanding business workflows
• Communicating uncertainty
• Explaining technical trade-offs
• Identifying where automation is inappropriate
• Working with product and domain teams
• Designing effective human oversight
• Learning from failed experiments

An effective AI agent engineer should be able to explain not only what they built, but also why the system was designed that way.

Interview Questions to Ask

Ask candidates questions that reveal their judgement and practical experience.

For example:

1. What agent or AI workflow have you personally built?
2. What business problem did it solve?
3. Why did the task require an agent rather than a simpler automation?
4. Which tools or systems could the agent access?
5. How did you measure whether it completed the task correctly?
6. What were the most common failure cases?
7. How did you control permissions and protect sensitive data?
8. When did the system ask for human approval?
9. How did you monitor quality, speed and cost after launch?
10. What would you change if you built it again?

Strong candidates should discuss limitations and failures clearly.

Be cautious if every project is described as completely successful.

Real production AI work normally involves difficult decisions about reliability, data, security, cost and user behaviour.

Nindar’s guide to verifying real AI engineering experience provides additional questions for distinguishing practical builders from candidates who have only used AI tools.

Warning Signs When Hiring AI-Agent Talent

Watch for candidates who:

• Focus only on prompt writing
• Cannot explain how the agent was evaluated
• Have built demonstrations but no production systems
• Ignore data quality
• Treat autonomy as the goal
• Cannot explain failure handling
• Give the agent unnecessary access
• Have no approach to monitoring cost
• Avoid discussing security or privacy
• Cannot connect technical work to business outcomes
• Use popular terminology without clear examples
• Claim that human oversight is unnecessary

Knowing the names of AI frameworks does not prove that someone can build a useful agent.

Use skills-first hiring to assess relevant work samples, architectural decisions, evaluations and project results.

What Should the Team Deliver in the First 90 Days?

Do not measure the first 90 days by the number of AI features produced.

A useful early plan may include:

Days 1–30: Define the problem

• Document the existing workflow
• Identify users and stakeholders
• Define a limited use case
• Review available data
• Identify risks and permissions
• Set success measures

Days 31–60: Build and test

• Develop a controlled prototype
• Connect only the necessary tools
• Create realistic test scenarios
• Measure accuracy and task completion
• Review failure cases
• Collect feedback from users

Days 61–90: Prepare for controlled use

• Improve reliability
• Add monitoring and audit logs
• Confirm human-approval points
• Complete security and governance reviews
• Train initial users
• Launch to a limited group
• Compare results with the original process

The first objective should be a measurable improvement to one important workflow—not an agent that attempts to do everything.

Build the Team Around the Outcome

Companies do not need an AI agent simply because the technology is popular.

They need a solution to a real business problem.

The right team may include an AI engineer, software engineer, data specialist, product manager, security professional, governance specialist and domain expert.

However, the team’s size matters less than whether the necessary capabilities are covered.

Before hiring, answer three questions:

1. What specific task should the agent complete?
2. What could happen if it completes that task incorrectly?
3. Which skills are required to make the system useful, reliable and secure?

Clear answers will help your company choose the right talent and avoid building an expensive demonstration with no practical value.

Find AI Talent With Nindar

Nindar helps startups and growing technology companies recruit specialist AI, engineering and product talent across global markets.

Through specialist recruitment, executive search, RPO and talent mapping, we help employers define difficult roles, identify relevant talent and assess evidence of real technical experience.

Whether you need an AI agent engineer, LLM specialist, AI product leader or a complete technical team, Nindar can help you search beyond job titles and connect with professionals who understand production AI systems.

Building an AI agent and unsure which talent you need? Contact Nindar to discuss the problem your team is trying to solve.