TL;DR

• Entry-level tech hiring has dropped by over 70% in 2025, creating a critical talent pipeline challenge for the industry.
• AI adoption is accelerating, reshaping how junior engineers onboard, learn, and contribute to projects.
• Junior developers benefit from AI-powered tools that speed up learning, automate repetitive coding, and provide instant documentation access.
• Despite AI’s power, human mentorship and strategic oversight remain essential to prevent “Frankenstein code” and ensure quality.
• At Temelion, a deliberate blend of junior talent, AI support, and senior mentorship enables graduates to deliver production-ready code within their first week.
• CTO Sébastien Gilles stresses that AI should serve as a tool, not a replacement, with developers owning design and decision-making.
• Junior engineer Céline shares how AI helped accelerate her onboarding and learning, while hands-on mentorship guided her through complex design choices.
• This model offers a promising alternative to the scarcity and high cost of senior AI/ML talent—unlocking scalable, high-impact teams.

Read on for an insider perspective on how AI and human collaboration can redefine early-career development in tech

Ravio’s recent report on how AI is reshaping the 2025 tech job market has made ripples in many tech ecosystems across the inter-webs, and rightly so, the results are unsettling. Globally, we are at the helm of workforce shifts and role disruptions and these unprecedented times demand unprecedented strategies and productive introspection within the ecosystem. 

One insight that caught our attention is that entry-level hiring has dropped by more than 70% in the past year, driven largely by the increasing adoption of AI in software development and business operations. 

As per Ravio’s report, job opportunities for new graduates in Human Resources, Marketing, and Engineering have been hit particularly hard, with contractions ranging from 75% to over 80%. Entry-level roles have historically served as the foundational pipeline for training future leaders. With fewer such positions available, companies now confront succession planning gaps and a shrinking pool of mid-level professionals.

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Temelion’s Perspective: Graduates + AI + Mentorship = Scalable Innovation

The picture isn't entirely bleak. AI/ML expertise has emerged as a priority skillset in 2025, particularly at large companies rapidly scaling their capabilities. Business functions requiring strategic thinking, creativity, and cross-functional relationship building areas currently beyond AI's scope remain in demand.

At Temelion, we like to challenge this fastly developing status quo. We believe that we're presented with both, exciting opportunities and critical considerations, for junior developers. By giving junior engineers real ownership, AI support, and structured mentorship, we are making an attempt to prove early-career talent is a strategic advantage.

Don’t believe us (yet)? Keep reading! 

We started out with  intentionally building a mixed team of seasoned professionals and entry-level engineers. We thought it was critical to bring a fresh perspective. We were lucky that we had a (very) seasoned CTO at the helm of this matter. 

We spoke with Sébastien Gilles, our CTO and Co-founder and Céline Cottin, our Junior Machine Learning Engineer, to share their real-world insights on putting this vision into action. 

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Meet Céline: A Junior Machine Learning Engineer's Journey with AI

Céline joined Temelion with a master’s degree in Applied Statistics and Data Science. While well-versed in data, she had no prior experience building full-scale products. She was the company’s first full-time technical hire and deployed a production-ready API in her first week (bravo Céline)!

Q: What is your perspective on the current job market for junior talent?

Céline: “It’s really challenging right now. If you look at the report by APEC 2024, entry-level jobs, across all domains in France, dropped by 19%. Companies are hesitant to hire juniors due to perceived risk, and the competition is intense, especially, with so many AI-specialised graduates entering the field within the Paris region. Plus, the hiring process has become more layered and selective than ever.”

Q: As a junior talent entering the workforce at this critical juncture, what has been your broad perspective on using AI for programming?

Céline: “As a junior in a challenging job market, I feel grateful for this opportunity and strive to work efficiently while also avoiding overwhelm. I found AI invaluable during my learning process, particularly for:

• Generating basic, repetitive code
• Understanding broad concepts
• Reducing dependency on mentors for constant questions
• Avoiding excessive time spent on forums like Stack Overflow

However, I do advocate for a balanced approach, in general. While I appreciate AI as a support tool for small tasks and concept clarification, I believe juniors shouldn't rely on AI for complete code solutions. The learning process requires understanding what you are doing and overusing AI can hinder this crucial development phase.”

Q: How was your onboarding and upskilling experience at Temelion shaped by AI so far?

Céline: “My onboarding was hugely accelerated because AI helped me learn in real time. I could ask specific questions about our codebase or tools and get clear, instant answers. For upskilling, AI acts like a personal tutor, helping me explore both broad and deep technical topics. But it doesn’t replace the need for feedback from senior engineers. They help me understand context and make better design decisions.”

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A CTO’s Perspective: Sébastien Gilles on Leveraging AI 

Sébastien Gilles, CTO and co-founder of Temelion, has been at the forefront of hiring and developing engineering talent for over two decades. For further context, prior to Temelion, he co-founded three startups and held senior leadership roles, including as CTO and VP of Engineering, at multiple AI scale-ups, industries ranging from law enforcement and justice, to advertising and multimedia production. Starting with his days at Oxford university, where he completed his Phd in Image Processing, he’s had a passion for creating and scaling enterprise-grade AI solutions. 

So needless to say, he has a lot of data points to measure this tectonic shift within the tech landscape. 

Sébastien highlights that giving AI too much liberty can result in "Frankenstein software" – overly complex and potentially error-prone code that even the human developer struggles to comprehend. In practice, this means developers must decompose problems, understand AI-generated code, and use AI strategically as a tool—not as the architect.

Q: What advice do you have for early-career developers navigating this AI-heavy landscape?

Sébastien: “AI can be both a servant and a master. As a servant, it’s excellent at handling repetitive tasks, speeding up execution while you focus on design. As a master, it teaches and upskills you by explaining new domains, offering real-time feedback, and highlighting trade-offs. But no matter what, humans must own the architecture and decision-making. Developers must understand what AI generates.”

Q: How has onboarding and upskilling changed with AI over the years?

Sébastien: “There’s been a major shift, as one can imagine. AI systems, especially those using retrieval-augmented generation (RAG), make it easier for junior developers to onboard by giving access to internal documentation, codebases, and company practices. For upskilling, AI enables quicker mastery of new technologies. But here’s the catch: AI still has limits. It can’t replace nuanced conversations, mentorship, or the ‘infinite bandwidth’ of a senior engineer who adapts to your learning style. You still need that human exchange.”

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The Bottom Line: A Better Trade-Off, If We Do It Right

If companies structure their onboarding and upskilling processes intentionally: combining well-documented systems, retrieval-augmented AI tools, and active mentoring, then graduates can be up and running within a week, shipping high-quality code with confidence and context. At a time when senior AI/ML talent is scarce and costly, this approach isn’t just a workaround: IT IS THE smarter investment.

By giving early-career developers real ownership, AI-accelerated learning, and the right guardrails, we’re unlocking a high-leverage model:

Graduates + AI + Mentorship = Scalable, production-ready talent.

It’s not hypothetical: it’s how we build at Temelion. With Céline, equipped AI tools, and supporting her with mentorship from experts like Sébastien, we’ve created a resilient, fast-moving team culture.

Our AI solutions for AEC teams are grounded in the same philosophy: use AI to eliminate bottlenecks, but keep humans in control of creativity, quality, and strategy.

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Want to See AI-Human Collaboration in Action?

Temelion leverages AI to automate and simplify complex technical documentation for building design engineers, ensuring precision, compliance, and faster project delivery.

[Speak to our team]

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