By Siam 
Why Does Your Company Need a Team of AI Engineers
A strong team of AI engineers has become essential as companies navigate customers’ demand for faster service. According to a survey conducted by McKinsey, “Twenty-one percent of respondents reporting gen AI use by their organizations say their organizations have fundamentally redesigned at least some workflows.” Workflow re-design doesn’t happen overnight. The organization goes through a series of discussions with stakeholders and employees. Here, employees play a huge role. Typically, in these instances, businesses either outsource AI engineers or train their employees to make these workflow changes.
But often this is not the case. Organizations face a shortage of the right AI talent and the right AI development company when hiring. “While 75% of companies are adopting AI, only 35% of talent have received AI training in the last year.” Leaders want to build the right AI team, but challenges such as skill gaps, unclear hiring standards, and growing competition for specialists create roadblocks to business growth.
Even though companies globally are deploying AI, many are still struggling to configure the data generated by AI or to integrate their existing data with AI. This creates a dependency on skilled AI engineers who understand data science, algorithms, Python, and other AI-powered tools. As these skills become more scarce, companies find it difficult to hire the right team for their business.
Why is it So Hard to Recruit AI Engineers?
Hiring AI engineers has become one of the toughest recruitment tasks for any business today. The demand for skilled AI developers far outpaces the available talent, which explains why companies struggle even after long hiring cycles.
“Less than one in three (31%) Baby Boomers currently use AI, while nearly half (48%) of Gen Z are leveraging the technology”. This uneven adoption creates internal gaps in knowledge, training, and readiness to embrace AI.
The Reasons Behind the AI Skills Gap
Reasons why you are struggling to hire AI developers:
1. Demand is far more than supply
Most industries need AI engineers to integrate generative features, build machine learning models, and require strong MLOps support. Universities produce only a small portion of what the market needs. This widening AI talent shortage is the first barrier that slows recruitment.
2. Competition with large global employers
Startups and mid-sized companies often compete with the largest technology companies in the world. Firms like Google, Meta, Amazon, and Microsoft offer compensation packages that many businesses cannot match. Remote work has expanded this competition. A company hiring in Mumbai or Bengaluru is now up against employers in San Francisco or London for the same AI engineer. These pressures make the challenges in hiring AI talent even more pronounced.
3. Skills change faster than hiring cycles
AI skills shift quickly. Talent once focused on traditional machine learning must now understand generative models. NLP specialists need to work with large language models. Data scientists may require MLOps exposure. New roles continue to emerge, including prompt engineers, fine-tuning experts, agentic AI architects, AI safety engineers, and multimodal developers. Companies that rely on older job descriptions often fall behind, leading to common AI hiring mistakes.
4. Compensation expectations are rising
Salary expectations now start high, even at the entry level. Mid-level and senior AI engineers seek better pay, equity, and opportunities for research. Many want conference access and clear innovation plans. These expectations stretch hiring budgets and make the process harder to manage.
What this means for companies
The difficulty in hiring AI engineers is not only a talent issue. It affects how companies plan projects, set timelines, and develop long-term AI strategies. Firms that want to build an AI team must understand current hiring pressures before setting expectations. Some organizations also turn to AI development services or an AI development company to close the gap. Others explore outsourcing AI development when internal hiring becomes too slow or costly.
What Skills You Need to Look for in AI Engineers
These are some of the basic skill sets you need to look for in AI engineers:
- Core AI Knowledge: Experience with generative AI, predictive analytics, LLMs, NLP, ML, deep learning, and reinforcement learning.
- Data and Statistical Skills: Ability to work with data science fundamentals, statistics, algorithms, and model development.
- Software and Coding Ability: Strong programming skills and experience building, deploying, and maintaining AI systems.
- Privacy and Security Awareness: Understanding of security, privacy standards, and safe handling of sensitive data.
- Practical AI Techniques: Capability to handle prompt tuning, fine-tuning, and other applied AI tasks without needing to build from scratch.
- Cross-Functional Collaboration: Candidates should be comfortable working with subject matter experts, product teams, and user experience (UX) designers.
- Leadership and Strategy: For senior roles, knowledge of current AI trends and the ability to guide business decisions on hiring AI engineers or outsourcing to AI development companies.
- Practical AI Techniques: Capability to handle prompt tuning, fine-tuning, and other applied AI tasks without needing to build from scratch.
- Cross-Functional Collaboration: Candidates should be comfortable working with subject matter experts, product teams, and user experience (UX) designers.
- Leadership and Strategy: For senior roles, knowledge of current AI trends and the ability to guide business decisions on hiring AI engineers or outsourcing to AI development companies.
How AI Engineers Can Help you Eliminate AI-Related Challenges
Hiring AI engineers has become difficult for many companies because of rising costs, limited expertise, and the fast pace of new skill development. Before bringing in external AI talent, it is important to check the credibility of the AI software development company you plan to engage.
The right partner should not only supply AI engineers who can address your technical challenges but also present a clear record of solving complex AI problems for other organizations.
Once the partnership begins, their AI engineers will typically focus on challenges such as the following:
High Costs and Uncertain ROI
Many organizations hesitate to begin AI projects because leadership often struggles to assess expected returns. A capable AI development partner can help by offering flexible engagement models and using open-source technology to limit upfront expenses. Cloud-native development also keeps spending tied to actual usage. With the right expertise in place, teams often see clearer, measurable outcomes that point to stronger returns over time.
Poor Data Quality That Slows Down AI Adoption
AI systems depend on reliable data. Skilled AI engineering teams create complete data pipelines that ensure models receive consistent, accurate information. These solutions are designed with governance in mind to protect sensitive data and follow global compliance standards. Workflows can scale as needed without raising operating costs, while data remains private and under full organizational control.
Difficulty Connecting AI to Legacy Systems
Many companies struggle to integrate AI tools with older ERP, CRM, or custom platforms. Experienced development partners address this by building middleware and APIs that allow systems to communicate without disruption. Legacy platforms can be integrated directly or migrated to cloud environments where needed, reducing downtime and cutting overhead costs.
Final Thoughts
Hiring AI engineers and building an AI team is difficult for most companies today. The talent shortage, fast-changing skill requirements, and rising costs make it hard to build the right AI team at the right pace. Many businesses try to manage everything in-house, but the process often slows down projects and creates unexpected gaps.
A practical way forward is to mix internal talent with the right external support. Partnering with an experienced AI development company helps companies avoid common AI hiring mistakes, reduce delays, and access hard-to-find skills. Whether your goal is to strengthen your current team or outsource AI development for specific projects, the focus should always stay on what moves the business ahead.
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