How having the right team can help you develop the right LLM

Large Language Models (LLMs) are being used across industries to automate workflows, analyze complex data, and improve decision-making. Businesses in finance, healthcare, and legal services are integrating them into critical operations, shaping how AI supports everything from risk analysis to patient diagnostics.

As you look to build an LLM that is tailored to your needs, you need the right people to make it happen. Machine learning engineers and LLM programmers will handle the core AI functions. Still, without data specialists, compliance experts, and industry advisors, even the most advanced models can struggle to deliver real results. From ensuring security and regulatory compliance to refining accuracy for specific applications, every stage of development depends on having the right team in place.

In this guide, we’ll explore how to structure an AI team, the hiring challenges in AI talent acquisition, and how businesses can take a strategic approach to tech recruitment to secure the expertise needed for large language model architecture and deployment.

Why Developing an LLM Is a Team Effort 

LLMs are becoming a major force in business automation, research, and enterprise AI strategies, with applications expanding across manufacturing, supply chain management, cybersecurity, and scientific research. The World Economic Forum estimates that LLMs could influence up to 40% of working hours across industries, driving efficiency but also creating new challenges in talent acquisition and AI management. 

Many businesses adopt these models, but few are fully prepared for the expertise required to build, refine, and regulate them effectively.

How LLM Hiring Has Changed:

• 2020–2022: AI hiring was mostly focused on machine learning research and natural language processing (NLP) applications. Businesses working with AI often relied on pre-trained models, adapting them without building in-house expertise.
• 2023–2024: LLMs became enterprise-ready. Businesses moved beyond basic AI applications, leading to a surge in AI recruitment for data science, AI ethics, and industry-specific AI roles. Companies started to recognize that compliance, model security, and fine-tuning for real-world use cases required a broader mix of skills.
• 2025 and beyond: Businesses face a decision. Some will continue relying on pre-trained models, while others will invest in dedicated AI teams to develop models tailored to their industry. The companies that map out their recruitment strategy early will gain a competitive edge. Those that don’t will struggle with hiring gaps, retention issues, and regulatory challenges.

At this point, businesses should consider exactly what they want from their LLM, as this will dictate the team structure and expertise needed to bring it to life.

What do you actually need from your LLM?

There are two ways to approach this; your path determines the expertise required to make it work.

Option 1: Adapting an existing model
 Some companies take a pre-trained LLM and adjust it for their needs. This is the faster, lower-cost route, but it comes with trade-offs. General-purpose models lack industry-specific understanding, may struggle with compliance, and often require ongoing, prompt engineering to produce reliable results. If the goal is to integrate AI quickly but with limited control over its outputs, this can be a workable approach.

Option 2: Building a tailored LLM

For businesses that need accuracy, compliance, and scalability, a custom-built model is the better option. A tailored LLM is trained on industry-relevant data, regulatory frameworks, and specific business requirements, ensuring greater control, reliability, and efficiency. But this approach requires a well-structured AI team with the right mix of technical, operational, and compliance expertise.

If a business chooses to build a custom LLM, success depends on more than just the technology itself. Every stage, from training data selection to compliance oversight and deployment, requires expertise across multiple areas to ensure the model is accurate, scalable, and aligned with industry regulations.

Several factors make building a tailored LLM a team effort, requiring a well-structured approach to AI talent acquisition and team management:

• Data quality defines performance: Training data must be high-quality, structured, and relevant to avoid bias and unreliable outputs. Poor data leads to models that produce inaccurate or misleading results, affecting large language model applications in real-world settings.
• Regulatory compliance needs to be built in from the start: AI governance frameworks, including the EU AI Act, require businesses to ensure transparency, security, and fairness. Failing to embed compliance early can lead to legal risks and operational setbacks.
• Customization requires deep industry knowledge: A model built for healthcare must process medical terminology differently from one designed for financial forecasting. Without industry expertise, even well-trained LLM programmers may struggle to create outputs that are practical and reliable.
• Long-term maintenance is critical: AI models require ongoing monitoring, retraining, and optimization to remain effective. Without a structured team managing these updates, models can become outdated, inefficient, or misaligned with business objectives.

For businesses investing in a custom LLM, having the right team in place is just as important as the model itself. 

The Key Challenges in Building an LLM Team 

Your business has established that a personalized LLM is the best path forward. You’ve identified the need to grow your AI team to ensure your model is built, deployed, and maintained with the right expertise in place. However, hiring for large language model applications isn’t straightforward. The demand for specialists far outweighs supply, making AI recruitment highly competitive.

What challenges will you face when building your AI team?

Even businesses with clear AI strategies struggle with AI talent acquisition. The demand for LLM programmers, AI compliance specialists, and machine learning engineers continues to rise. Without a structured hiring strategy, companies risk delays, skill gaps, and compliance failures. These are the biggest obstacles businesses need to overcome:

Recruiting specialists in a competitive market

The market for tech recruitment is under pressure, with over 97 million AI-related jobs expected to be created by 2025. Despite this, 76% of senior IT leaders say there is already a shortage of skilled professionals in AI team management and development. 

Businesses hiring for LLM programmers and specialists in large language model architecture are competing with tech firms, research institutions, and global enterprises for the same expertise. 

Retention and burnout

The high demand for AI recruitment means teams are under increasing pressure. 71% of full-time employees report feeling burned out, and those who frequently work with AI experience a 45% higher burnout rate. Without effective AI team management, businesses risk losing key talent, leading to delays, knowledge gaps, and increased hiring costs.

Cross-disciplinary gaps

LLM development requires close collaboration between technical experts, industry specialists, and compliance teams, but many companies struggle to connect these disciplines. 24% of employees worry AI could replace aspects of their role, which can create resistance to collaboration. Without structured teamwork, businesses risk developing models that are misaligned with real-world applications and require costly rework.

Ethical and legal complexities

Ethical considerations in AI are now a core concern for businesses. AI regulations such as the EU AI Act continue to evolve, with 67% of talent acquisition professionals citing concerns about AI biases and inaccuracies. Without dedicated AI ethics specialists, businesses risk non-compliance, reputational damage, and challenges in deploying large language model applications effectively.

Building the Right Team for LLM Development 

A successful LLM development team is diverse, strategic, and collaborative. You’ve identified that a tailored LLM is the right choice for your business, and you understand the hiring challenges that come with it. 

The next step is assembling a team that ensures your model delivers real business impact. But which specialists should you bring on board? Here’s a breakdown of the key roles and steps to building the right team.

Who You Need: Key Roles in LLM Development

Each specialist plays a unique role in building, refining, and deploying your LLM, ensuring it is accurate, scalable, compliant, and functional within business applications.

AI/ML Engineer: Building the Model Architecture

Machine learning engineers design, train, and fine-tune the large language model architecture. Their expertise ensures your LLM efficiently processes data, learns from interactions, and continuously improves its accuracy.

Key skills: Deep learning frameworks (TensorFlow, PyTorch), reinforcement learning, model optimization.
Average salary in Europe: €85,000–€125,000

Data Scientist: Structuring and Preparing Training Data

A well-trained LLM relies on high-quality, unbiased data. Data scientists handle data collection, cleaning, and feature engineering, ensuring the model learns from diverse, structured datasets to prevent bias and misinformation.

Key skills: Data preprocessing, statistical modeling, large-scale dataset handling.
Average salary in Europe: €75,000–€85,000

NLP Engineer: Refining Language Processing

Natural Language Processing (NLP) engineers enhance how LLMs interpret language, structure responses, and maintain contextual awareness. They are critical for optimizing user interactions, AI-generated content, and language understanding.

Key skills: NLP algorithms, sequence modeling, contextual embeddings.
Average salary in Europe: €80,000–€110,000

AI Ethics & Compliance Specialist: Managing Ethical & Legal Risks

With regulations such as the EU AI Act, businesses must ensure LLMs operate transparently, fairly, and in compliance with legal standards. These specialists mitigate risk, address AI bias, and ensure models adhere to data protection laws.

Key skills: AI governance, compliance frameworks, bias mitigation.
Average salary in Europe: €75,000–€100,000

Cloud & Infrastructure Engineer: Scaling LLM Deployments

Scaling an LLM requires efficient cloud computing resources. Cloud engineers optimize deployment, manage compute costs, and ensure high availability, preventing downtime and inefficiencies.

Key skills: Cloud architecture (AWS, Azure, Google Cloud), Kubernetes, MLOps.
Average salary in Europe: €80,000–€115,000

Cybersecurity Engineer: Protecting AI Models and Data

LLMs must be secured against cyber threats such as data poisoning, unauthorized access, and adversarial attacks. Security engineers implement encryption, access controls, and threat detection protocols to safeguard AI applications.

Key skills: AI security, threat detection, data encryption.
Average salary in Europe: €85,000–€120,000

Ready to build your team now? Get in touch with us at Montreal Associates today - the IT Staffing Specialists since 1978.

Timing Your LLM Team: When to Bring in the Right Expertise

We’ve discussed the specialists your LLM project needs. Now let’s focus on timing. When should each role be brought in to make sure your model gets developed and deployed successfully?

Each stage of LLM development requires specific expertise, and the right talent must be brought in at the right time. Understanding the AI recruitment landscape and structuring your team according to project needs can help you avoid delays and challenges along the way.

Stage 1: Early Development

At the beginning of your LLM project, the focus is on setting up infrastructure and ensuring compliance with ethical considerations in AI. This is when you need AI ethics specialists to guide regulatory compliance and prevent any potential biases in the model. You’ll also need cloud engineers to set up and manage the environment where the LLM will operate, ensuring everything is in place to handle large-scale applications.

Stage 2: Model Creation

As the development phase kicks off, AI/ML engineers come in to create the large language model architecture. They are responsible for building and fine-tuning the model. Data scientists work alongside them, structuring and cleaning the data that will train the model, making sure it’s both relevant and high-quality. NLP engineers focus on refining language processing to ensure the model can handle complex text and generate meaningful, contextually aware responses.

Stage 3: Scaling and Deployment

Once the model is trained, the focus shifts to deployment and scaling. Cloud engineers are essential here to ensure that the model is scalable, integrated, and performs efficiently as the business grows. As the LLM gets deployed, AI/ML engineers will continue to refine the model, making improvements based on real-world feedback.

By ensuring that your recruitment strategy is aligned with each stage of development, businesses can build a more effective AI team that helps drive success at every point in the process. AI talent acquisition should be planned ahead to meet the project’s evolving needs, ensuring you’re not left behind as your LLM grows and scales.

Develop Your Next LLM With Confidence 

The success of any LLM project hinges not only on the technology but also on the team behind it. A strong, collaborative, and ethically grounded team can turn an ambitious vision into a meaningful reality. Building a successful LLM requires the right specialists, aligned with your business goals, working together across all stages of development.

Even with a clear hiring strategy, securing the right talent for LLM programming and AI recruitment remains a challenge. Businesses need to be mindful of several factors:

• AI talent acquisition strategies – The increasing demand for LLM programmers, AI compliance specialists, and machine learning experts makes it crucial to start recruitment early.
• Project timelines – Without a clear plan in place, businesses risk delays in development and talent shortages that can hinder progress.
• Retention and team structure – AI teams often operate under high pressure. Clear career development opportunities and effective AI team management are essential to prevent burnout and turnover.

The value of partnering with a specialist global recruiter

Partnering with a global technology recruiter like Montreal Associates can significantly ease the hiring process. Specialist recruiters with expertise in AI recruitment can help businesses:

• Source the right specialists for roles like LLM programming and AI ethics.
• Reduce hiring timelines, ensuring you stay on track with large language model applications.
• Align your recruitment strategy with long-term business goals, ensuring you have the right people in place to drive success.

Given the AI talent shortage and growing competition in the field, having access to a pre-vetted talent pool ensures that your team is equipped to tackle these challenges head-on, ultimately securing the right expertise at the right time for LLM development.

Key Takeaways: AI Talent Acquisition for LLM Development

The success of an LLM project doesn’t rest solely on technology. It’s about the team that designs, builds, and refines it. A tailored model requires a well-coordinated effort from specialists across multiple disciplines to ensure that it not only functions but drives business value.

As businesses look to integrate these models into their core operations, it’s easy to underestimate the scale of talent required. From LLM programmers and AI compliance specialists to data scientists and machine learning engineers, securing the right talent is the foundation of success.

The challenge is clear: to succeed with your development, you need a team that brings not just technical skills, but a deep understanding of industry-specific needs, compliance standards, and ongoing model optimization. The AI recruitment market is competitive, and securing the right specialists at the right time will set your business apart.

Is your business ready to secure the best AI talent?

Ready to build your LLM with the right team in place? At Montreal Associates, we specialize in AI talent acquisition to help you secure the right professionals at every stage of LLM development. From LLM programmers to AI compliance experts, we provide the sourcing expertise you need to make your project a success.

Let’s talk about how we can help you build the right team and bring your LLM vision to life.