How to Attract LLM Developers Amidst the AI Boom


The job market for LLM developers will continue thriving for several years. Here are four tips to help ensure your company will have a stronger shot at attracting and retaining top talent.

The release of ChatGPT ignited a spark seen and felt across the world – where now every business is talking about and looking to understand and integrate AI in some way. We can thank LLM developers, as they are the masterminds behind the recent generative AI tools and advancements hitting the market.

Forbes Advisor reports that 64% of business owners believe AI has the potential to improve customer relationships, indicating a positive outlook on the role of AI in enhancing client interactions. As a result, the demand for large language model (LLM) developers has grown exponentially – however, the pace at which these LLM developers have been educated has not kept up with the speed at which the field continues to develop. Think of the rise of LLMs as the rise of the internet – except the changes that were made over the last 50 years will now happen in a fraction of the time. 

Earlier this year, the 2023 State of the CIO report found that 41% of IT leaders planned to ramp up hiring and that new hires are anticipated in cybersecurity (39%), app development (30%), and data science/analytics (30%) positions. LMM developers fall within this desired group, and they, in particular, require a special set of skills and training. As it’s an emerging space, it’s not a field widely taught at universities just yet. However, with a limited talent pool, there are a few ways to successfully attract and retain top LLM talent in today’s environment.

See also: Conversational AI Will Continue on Its Growth Trajectory

The list of skills needed to be a LMM developer is not short

A successful role as an LMM developer requires several skills, including knowledge in machine learning theory, natural language processing, and understanding, the ability to train and serve large architectures that require multiple Graphics Processing Units (GPUs), prompt engineering, and an understanding of the goal and domain of the LLM and the dataset that should be used. Developers who combine all these skills are hard to come by.

In addition to these capabilities, LLM developers require training in several areas, specifically machine learning, including transformers and their variants (encoder-only, encoder-decoder, decoder-only), autoregressive models, adapters, reinforcement learning, and many more. 

To support this, several universities and institutions that I am aware that offer machine learning and data science/engineering courses are gradually adapting them to incorporate LLMs. While the AI journey started a long time ago for some, the current boom happened very quickly, so I anticipate universities will work to keep pace and increase their offerings in this space.

Four ways to attract talent top LLM developer talent

Given the extensive requirements, skills, and training needed to be an LMM developer, finding and retaining LLM talent can be a challenging prospect. However, I see four ways organizations can lure these sought-after professionals amid today’s challenging landscape:

  1. Have a coherent and challenging vision about LLMs. This is top of the list, as it plays an important role in exciting and attracting developer talent. 
  2. Provide good, clean data or the means to collect it. Without this, the job of the LLM becomes mostly about working backwards in order to move forward. 
  3. Have a strong MLOps team to support LLM developers. No one wants to come in as a one-man show.
  4. Offer a competitive salary. Though it’s a challenging economy, the role of an LLM developer requires a hefty list of skills, so it’s important to recognize this and offer a competitive salary.

With these four tips, your company will have a stronger shot of attracting and retaining top LLM developer talent. My outlook is that the job market for LLM developers will continue thriving for several years as companies realize that developing and serving their own LLMs is the way to get ahead and stay ahead in this competitive market.

Themos Stafylakis

About Themos Stafylakis

Themos Stafylakis has spent the past 13 years working in the fields of speaker recognition, segmentation, clustering, and diarization. He is currently Head of Machine Learning and Voice Biometrics at Omilia - Conversational Intelligence. The company is a conversational AI pioneer, delivering high-quality, automated voice and chat solutions for customer service. Omilia owns and provides state-of-the-art technology in Conversational AI, enabling clients to improve their customer experience, shorten response times, and reduce costs.

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