Create your own AI Engineering Team

learn all about roles and skills to boost your business

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Having a squad of AI Engineers will make a huge difference for your organization. They will be able to create custom AI solutions leveraging LLMs and multiple components essential for creating Generative AI apps. All solutions custom for your own business.

Today I’ll cover:

  • The Power of Custom AI Solutions

  • The roles needed

  • How to attract talent

  • Essential skills

  • Team structure

  • Talent retention strategies

Let’s Dive In! 🤿

The Power of Custom AI Solutions

Imagine a suite of AI-driven solutions tailor-made for your business. With a proficient team of AI engineers, the possibilities extend far beyond conventional pre-built solutions. Those solutions offer convenience but they often lack the nuance and personalization needed to truly address your unique business challenges.

To truly embrace AI, you need to embrace in-house AI development.

A dedicated team can craft custom solutions tailored to your specific workflows, data, and objectives. Imagine:

  • Intuitive, Chat-Based Document Search: Say goodbye to searching through endless documents. An AI-powered search tool, accessible through a simple chat interface, can answer your questions and pull relevant information instantly.

  • Autonomous Task Management: Repetitive tasks bog down your team. Build AI agents that handle them seamlessly, freeing up human talent for more strategic work. Think scheduling, data entry, or even customer service interactions.

  • Personalized Customer Experiences: Delight your customers with AI-driven chatbots that answer their questions, recommend products, and resolve issues efficiently. Personalization goes beyond simple interactions, with AI analyzing customer data to tailor experiences and increase satisfaction.

  • Content Creation on Autopilot: Generate high-quality reports, marketing materials, or even social media posts using AI algorithms trained on your brand voice and style. Save time and resources while producing engaging content that resonates with your audience.

These are just a few examples. The possibilities are endless, limited only by your imagination and the expertise of your AI engineering team.

Building Your Dream Team: The Road to AI Success

Excited to tap into the power of custom AI? Let’s navigate the journey to assembling your dream AI team, where expertise meets innovation.

Essential Roles

In the rapidly evolving AI landscape, the shift towards leveraging LLMs has transformed the composition and focus of AI teams:

Traditional ML Team Roles:

  • Machine Learning Engineers build and optimize AI models.

  • Data Scientists ensure model accuracy through data analysis.

  • Software Engineers integrate AI models with existing systems.

  • UX/UI Designers develop user-friendly interfaces for AI tools.

New Era Adjustments:

  • AI Engineers: Now, this role merges the responsibilities of Machine Learning Engineers and Software Engineers. These professionals select, integrate, and potentially fine-tune pre-trained LLMs, focusing on application and customization rather than building models from scratch. Their expertise in LLMs simplifies connecting AI to systems and crafting effective prompts.

  • UX/UI Designers: Their role evolves to address more complex human-AI interactions, ensuring that interfaces are not only intuitive but also capable of facilitating sophisticated engagements like conversational UIs and adaptive feedback mechanisms.

The Impact of Pre-trained LLMs: Pre-trained LLMs have democratized AI by making powerful tools accessible without the need for extensive data or computational resources. This shift allows AI teams to concentrate on application, integration, and customization, speeding up development and innovation across sectors. It represents a significant step towards AI solutions that are more adaptable, intuitive, and personalized, underscoring a new era in AI technology's democratization.

Data Scientists are not becoming obsolete; their skills in interpreting AI data, fine-tuning models, and is still important. My recommendation though is to find AI Engineers who should also know the basics of model tuning to complement this expertise.

With LLMs, we have a leaner team and a more focused approach.

Everyone should learn AI

Attracting Top Talent

The competition for AI talent is fierce, but with the right strategy, you can attract the best. Here are some prime hunting grounds:

  • AI-focused job boards: Platforms like LinkedIn, Hired, and Indeed feature dedicated sections for AI job listings.

  • Professional networking sites: LinkedIn excels as a resource for finding AI professionals in industry-specific groups and communities.

  • Technical Community: GitHub serves as a prime spot for identifying talent through AI projects and contributions.

  • University career fairs and programs: Engage with top universities known for AI programs, which host career fairs and have job boards for graduates.

  • AI conferences and meetups: These gatherings are ideal for networking with potential hires and promoting your company culture.

  • Employee referrals: Utilize your team’s network by incentivizing referrals to attract compatible and skilled candidates.

Salary Ranges: Competitive Offers for Top Talent

Compensation plays a crucial role in attracting and retaining AI talent. While specific figures can vary based on location, experience, and specialization, here's a general guide:

Estimate of salaries for AI Engineers

Remember, competitive salary shouldn't be your only tool. Offer comprehensive benefits packages, opportunities for professional development, and a work environment that fosters creativity and innovation.

Building a Cohesive Unit

Harnessing AI's power starts with assembling a dream team that combines technical skills with a collaborative spirit. Here's how to build a cohesive unit:

  • Clear roles and responsibilities avoid confusion and ensure everyone is working towards shared goals.

  • Open communication and collaboration foster knowledge-sharing and problem-solving across disciplines, especially with other SMEs on the business side.

  • Continuous learning and development keep your team at the forefront of the ever-evolving AI landscape.

Creating a balanced AI team structure is essential for organizations looking to leverage artificial intelligence effectively. Below is a recommended table outlining the optimal number of AI Engineers relative to overall organizational size, and guidance on when to appoint a Head of AI.

The optimal number of AI Engineers based on Organization Size

The importance of the Head of AI role

The Head of AI is responsible for the overall strategy, development, and implementation of AI initiatives within an organization or group.

The primary focus is to ensure that AI is being used effectively to drive business value and achieve the organization's goals. You need AI leaders who understand that AI projects require an Agile AI, quick iteration methodology where you start small and grow the implementations with validation over time.

How to Develop AI Solutions


  1. Lead ai teams

  2. Driving AI research and development

  3. ensuring ethical and responsible use of AI

  4. communicating about ai

The Importance of a Head of AI

Keeping Your AI Heroes Happy

Maximizing the potential of AI depends on the happiness and engagement of your team. Here's a streamlined strategy to ensure your AI professionals remain motivated and innovative:

  • Regular feedback and recognition show appreciation for their contributions and foster motivation.

  • Opportunities for career growth and advancement keep them engaged and challenged.

  • Work-life balance enables them to perform at their best while maintaining personal well-being.

Tactical actions for enhancing your AI team’s motivation

With the right team in place, your organization can unlock the immense potential of custom AI solutions. I hope you enjoyed today’s edition.

Enjoy the weekend folks,

Armand 🚀

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