Should You Train Internal Engineers or Hire External AI Talent?
By
Samantha Cox
•
Jun 17, 2025
Should you train your own AI engineers, or bring in outside firepower? Internal engineers keep your systems running, your models efficient, and your teams unblocked. But when it comes to scaling AI capabilities, is it smarter to build from within or hire talent that’s already done it elsewhere? This piece breaks down what internal engineers do and what to consider when deciding how to grow your AI team.
Key Takeaways
Internal engineers are vital for optimizing organizational efficiency and driving technological innovation through collaboration and problem-solving.
Training internal engineers fosters a skilled workforce that aligns with company culture, while hiring external AI talent provides immediate expertise but can be costlier.
Integrating AI in hiring processes streamlines recruitment, enhances candidate quality, and improves decision-making, making the hiring experience more efficient and transparent.
What Does an Internal Engineer Do?
Internal engineers are the backbone of any tech company. They are responsible for maintaining and optimizing internal systems to support organizational efficiency. Whether it’s ensuring smooth software development or automating processes, internal engineers play a pivotal role in the technological landscape of a company.
Collaboration is at the heart of the internal engineer’s role. They work closely with various departments, providing technical insights and solutions that align with business needs. This cross-functional interaction ensures that the technical solutions they develop are not only innovative but also practical and aligned with the company’s objectives. Automating internal processes enhances operational efficiency, streamlining everyday tasks and making them quicker.
Moreover, internal engineers are the go-to team for troubleshooting issues and providing technical support to other teams. Their ability to swiftly identify and resolve problems ensures that the organization’s operations run smoothly, minimizing downtime and maximizing productivity. Internal engineers are the unsung heroes maintaining the technological gears of a company.
Steps to Becoming an Internal Engineer
A career as an internal engineer is rewarding, requiring a mix of education, practical experience, and certifications. The journey typically starts with obtaining a bachelor’s degree in computer science or a related field, which lays the foundation for a deep understanding of software engineering and AI engineering principles.
Practical experience is equally crucial. Engaging in:
Hands-on projects
Internships
Real-world applications
Earning certifications in AI, machine learning, and data science significantly enhances qualifications and marketability, making one a competitive job candidate.
Obtain a Bachelor's Degree
The journey starts with a bachelor’s degree in computer science or a related field. This formal education provides a strong foundation in software engineering, programming languages, and the core principles of AI and machine learning, preparing students to become artificial intelligence engineers. Degrees in STEM or quantitative fields are particularly preferred, as they equip students with the analytical and problem-solving skills necessary for this role, including prompt engineering.
Further specialization through a master’s degree can also be beneficial.
Gain Practical Experience
Practical experience is fundamental to a successful career in internal engineering. Projects, internships, and hackathons provide invaluable hands-on experience. Activities like these allow aspiring AI engineers to tackle real-world challenges and develop a deep understanding of how theoretical concepts apply in practical scenarios. For example, becoming an AI engineer by participating in open-source projects or enrolling in bootcamps can offer a rewarding career path by building proficiency in key skills.
Moreover, hands-on labs and practical projects, especially those focusing on machine learning, deep learning, and AI, are essential for developing technical skills. Platforms like Google Cloud offer real-world machine learning experiences, enabling machine learning engineers to design, build, and maintain machine learning systems.
Earn Relevant Certifications
Certifications are crucial for validating an internal engineer’s skills and knowledge. Earning certifications in AI, machine learning, and data science demonstrates a commitment to staying updated with the latest technologies and trends. These certifications are particularly valuable as they cover specific areas of expertise that are essential for internal engineers.
Structured online courses in Generative AI and other specialized fields are available for both beginners and advanced professionals to enhance their skillset. Resources like Google Cloud provide free monthly credits, hands-on labs, and skill badges, making it easier for engineers to become AI engineers and get the necessary certifications to advance their careers.
Key Skills for Internal Engineers
Internal engineers need a unique blend of technical and soft skills to excel in their roles. Technical skills are paramount, as they enable engineers to design, implement, and maintain complex systems. However, soft skills are equally important, as they facilitate effective communication and collaboration with various stakeholders within the organization.
Technical skills encompass proficiency in programming languages like Python, R, and Java, alongside a solid understanding of data science and software engineering principles. Soft skills, on the other hand, encompass the ability to convey technical concepts clearly, think creatively, and solve complex problems.
Technical Skills
The technical skills required for internal software developers are extensive and varied. Proficiency in Python, R, and Java is essential for software development and data science tasks. Additionally, a strong foundation in subjects like linear algebra, probability, and statistics is crucial for understanding and implementing machine learning algorithms and data engineering as part of a data science team.
Familiarity with tools such as:
LangChain
Hugging Face
Llama
These tools enable engineers to build and optimize machine learning models, contributing to the development of efficient and effective machine learning solutions through natural language processing, large language models, and deep learning algorithms.
Overall, technical skills are the backbone of an internal engineer’s ability to create and maintain robust systems.
Soft Skills
While technical skills are critical, soft skills are equally essential for internal engineers. Effective communication is essential for explaining complex technical concepts to both technical and non-technical stakeholders. This ability to bridge the gap between different teams ensures that everyone is on the same page and working towards common goals.
Creative and analytical thinking are also important, as they enable engineers to devise innovative solutions to real-world challenges. Critical thinking is valuable for refining AI models and ensuring their accuracy and reliability. In essence, soft skills complement technical expertise, making internal engineers well-rounded professionals capable of tackling diverse challenges.
Training Internal Engineers vs. Hiring External AI Talent
When it’s time to build out an AI team, many companies face the same question: should we train our own engineers or bring in outside experts? Both paths have their upsides. Training your team means they’ll be deeply aligned with your company’s goals and culture. Hiring external talent brings in ready-to-go expertise and new ways of thinking, but often at a higher cost.
The right choice depends on what matters most to you: speed, budget, long-term growth, or consistency. We’ll walk through the trade-offs so you can figure out what makes the most sense for your team.
Cost Comparison
Cost is a significant factor when deciding between training internal engineers and hiring external AI talent. Training internal engineers requires ongoing investment in education, practical experience, and certifications. This approach can lead to long-term benefits such as increased operational efficiency and strong alignment with company culture.
In contrast, hiring external AI talent often requires offering competitive salaries to attract specialized and experienced professionals. While this can be more costly upfront, it provides immediate access to expertise and can accelerate project timelines.
Here’s a table comparing the cost aspects of both approaches:
Cost Aspect | Training Internal Engineers | Hiring External AI Talent |
Education | Ongoing investment | Not applicable |
Salaries | Competitive but gradual | High upfront |
Operational Efficiency | Long-term benefits | Immediate impact |
Time Investment
Time investment is another critical consideration. Training internal engineers typically requires a longer timeframe for skill development through education and practical experience. This time investment results in a deeply integrated team understanding the company’s unique challenges and goals.
On the other hand, hiring external AI talent allows for immediate onboarding and quicker project initiation. This approach can be particularly beneficial for organizations facing urgent hiring challenges or those needing to ramp up their AI capabilities rapidly. However, it may take time for external hires to fully integrate into the company culture and processes.
Quality and Consistency
Quality and consistency of talent are crucial in the decision-making process. Training internal engineers ensures skills developed align closely with the company’s needs and culture. This can lead to higher quality and more consistent performance over time.
In contrast, hiring external AI talent can bring immediate expertise and high-quality candidates who are already well-versed in the latest technologies. However, the consistency of performance may vary as external hires adapt to the company’s environment.
Ultimately, the choice between training and hiring depends on the organization’s business objectives and long-term strategic goals.
The Role of AI in Streamlining Hiring Processes
Artificial intelligence changes the hiring process, making it more efficient and effective. Companies using AI in recruitment report significant improvements in hire quality and recruitment speed. AI technologies can automate repetitive tasks, provide data-driven insights, and enhance candidate engagement, leading to a more streamlined hiring process.
Integrating AI into hiring processes reduces bias, improves decision-making, and enhances overall efficiency. This benefits the company and creates a more positive candidate experience, making the recruitment process smoother and more transparent.
Screening and Evaluation
AI screening tools can transform the way companies evaluate candidates by:
Analyzing resumes and assessing candidates’ skills through automated evaluations, significantly reducing recruiters’ workload.
Facilitating a more efficient shortlisting process by automatically ranking candidates based on predefined criteria.
Ensuring only the most suitable candidates are considered.
AI can spot red flags in candidate applications by cross-checking details and looking for anything that doesn’t add up. It’s a way to catch potential fraud early and make sure only legit, qualified candidates move forward, saving teams time and headaches down the line.
Fraud Detection
Fraud detection is a critical aspect of the hiring process, and AI plays a significant role in this area. AI analyzes application data and flags discrepancies, identifying fraudulent submissions and ensuring candidates meet necessary qualifications. This saves recruiters time and enhances the hiring process’s integrity.
Automating certain screening tasks with AI allows recruiters to focus on assessing candidate authenticity and fit, improving their overall productivity. This leads to a more efficient and reliable recruitment process, ensuring that only the best candidates are selected.
Enhancing Recruiter Productivity
AI tools can significantly enhance recruiter productivity by automating specific tasks such as resume screening and interview scheduling. This allows recruiters to focus on more strategic aspects of hiring, such as building relationships with candidates and personalizing the recruitment experience.
AI-powered predictive analytics help recruiters identify high-potential candidates and reduce time to hire. Leveraging AI for process optimization and candidate communication enhances recruiters’ efficiency and effectiveness, leading to better hiring outcomes.
Implementing AI in Your Hiring Stack
Integrating AI into your hiring stack offers benefits like enhanced efficiency, reduced time on repetitive tasks, and improved decision-making capabilities. It’s crucial to ensure this integration aligns with organizational goals and maintains human oversight in decision-making.
To successfully implement AI in your recruitment strategy, you need to choose the right tools, ensure their ethical use, and provide adequate training for your recruitment team. By doing so, you can leverage AI to streamline your hiring processes while maintaining control over critical decisions.
Choosing the Right AI Tools
Choosing the right AI tools is essential for enhancing recruitment efficiency and aligning with specific organizational goals. These tools should offer features such as candidate management, data integration, and automated resume screening to streamline the hiring process. It’s also important to consider the compatibility of these tools with your existing systems and their ability to fulfill your recruitment objectives.
Ensuring ethical AI use in hiring is crucial for maintaining fairness and avoiding bias. This involves regularly evaluating AI algorithms for compliance with ethical standards and making adjustments as needed to ensure transparency and accountability.
Ensuring Ethical AI Use
Ethical AI use in hiring is crucial to maintaining fairness and transparency in recruitment. Key steps include:
Establishing clear AI guidelines
Regularly evaluating algorithms for bias
Ensuring AI tools comply with ethical standards
Preventing AI from introducing or perpetuating biases in candidate evaluations.
Regular audits and updates to AI systems maintain their integrity and ensure alignment with company values and objectives. By prioritizing ethical considerations, organizations can leverage AI to enhance their hiring processes without compromising fairness or accountability.
Training Your Recruitment Team
Providing ongoing training for your recruitment team is crucial for the effective use of AI tools. Training should cover the technical aspects of AI tools and their ethical implications in hiring. This ensures recruiters can leverage AI technology effectively and make informed decisions.
By automating administrative tasks, AI allows recruiters to focus on building meaningful candidate relationships and engaging in strategic activities. This not only enhances recruiter productivity but also improves the overall candidate experience, leading to better hiring outcomes.
Summary
In conclusion, both training internal engineers and hiring external AI talent have their unique advantages and challenges. Training internal engineers allows for tailored skill development and stronger alignment with company culture, while hiring external talent offers immediate expertise and fresh perspectives. Key considerations such as cost, time investment, and quality should guide your decision-making process.
Artificial intelligence plays a crucial role in streamlining hiring processes, enhancing efficiency, reducing bias, and improving candidate engagement. By carefully selecting AI tools, ensuring their ethical use, and providing adequate training for your recruitment team, you can leverage AI to achieve better hiring outcomes. Ultimately, the right approach depends on your organization’s specific needs and long-term strategic goals.
