A Career Guide for Engineers Entering the Machine Learning Space
You may have already explored AI models, developed side projects and are now considering a transition into a career in machine learning — whether through genuine interest or as a response to market demand.
Moving from exploratory work to a professional role requires more than enthusiasm; it demands a structured, strategic approach.
The demand for AI talent has grown at an exceptional pace, with AI-related job postings increasing by an estimated 74% annually. Organisations across industries are seeking engineers who can not only understand the theory but also design, implement, and optimise AI solutions that deliver measurable results.
The market is highly competitive and the opportunities are significant — provided you can demonstrate the right skills and capabilities.
The Market Is Booming - and Desperate for Talent
The demand for AI professionals is surging. One report notes a 25.2% increase in AI-related job listings in Q1 2025 compared to Q1 2024, alongside an 8.8% rise over the previous quarter, and median salaries climbing toward $157K USD, a sign employers are serious about investing in talent.
Another global analysis confirms tech job postings mentioning AI soared by 114.8% in 2023, 120.6% in 2024, and continued growth of 56.1% year to date in 2025 - showing this isn’t a fad, it’s a sustained shift.
Meanwhile, a stark skill shortage is playing out across industries. Over half of IT leaders cite undersupply of AI talent - the fastest surge of demand in over 15 years. Even in finance, major banks like JPMorgan and Citi have ramped AI hiring by 13% in the past six months alone, underscoring how essential AI native talent has become.
What Employers Want in 2025
Employers in 2025 are especially keen on engineers proficient in Python, adept with frameworks like TensorFlow, experienced in cloud deployment, and capable of translating complex ML solutions into tangible business outcomes.
Organisations are prioritising engineers who combine technical skills with domain knowledge, understanding not only how to build models, but also why they matter commercially.
In 2025, the skills employers prioritise are clear: Python proficiency appears in 71% of AI‑related job listings, while cloud platforms like AWS and Azure, alongside frameworks like TensorFlow and PyTorch, feature heavily in top roles.
Demand isn’t limited to narrow technical paths: hybrid capabilities are just as critical. Employers increasingly want candidates who blend machine learning with strategic thinking, domain insight, and influence capabilities.
Certifications vs Projects: What's More Valuable?
When it comes to standing out, many aspiring AI engineers wonder if they should pursue formal certifications or focus on practical projects.
Here’s a reality check: projects almost always speak louder than certifications alone. While credentials can validate foundational knowledge, employers want evidence that you can tackle real-world challenges effectively. Projects that demonstrate clear business value, practical problem-solving solving and genuine creativity move the needle significantly more than theoretical coursework.
The data is telling. Job markets are shifting toward skill-based hiring models where proven experience eclipses formal degrees or certificates. One study found that mentions of university requirements fell by 15%, while AI skills now command wage premiums of up to 23% - outpacing even advanced academic credentials.
If you can’t exercise these new skills within your existing role, to truly impress potential employers, consider engaging in impactful side projects. Participating actively in open source communities, showcasing your skills on platforms like Kaggle, and developing real-world applications that solve actual industry problems, provides clear proof of your capability.
These types of projects illustrate not only your technical abilities but also your initiative (tick!) and collaborative mindset (tick!)
Preparing for AI Interviews
Landing the interview is just the first hurdle. AI role interviews are notoriously multifaceted, typically involving discussions about specific business cases, detailed mathematics underpinning models, and practical conversations around your previous project experience.
Preparing effectively means anticipating these discussions and being ready to articulate clearly how your technical solutions translate into tangible business value.
Avoid The Common Pitfalls
However, there are common pitfalls to be wary of. Overengineering solutions without considering practical applicability can be a red flag. Likewise, submitting jargon-heavy/Chat GPT-generated CVs can confuse rather than impress hiring managers. Always highlight how your work directly impacts business objectives, steering clear of technical jargon unless necessary, and make sure to communicate your achievements.
Finding Opportunities
As for where to find the best roles, many immediately gravitate towards the big names in tech - FAANG companies are an obvious target. Yet, don't overlook the abundance of opportunities within startups, buy-side financial firms, and other niche consultancies, where smaller teams offer greater flexibility, quicker career growth, and meaningful hands-on experience in diverse projects. The comp is comparable to some bigger players, too!
Your AI Career Roadmap
To sum up, transitioning into an AI career successfully involves strategically showcasing your skills through impactful projects, thoroughly preparing for multifaceted interviews, and effectively communicating your value beyond technical expertise alone.
Get in touch with us and keep an eye on our Orbis job board for all the latest AI roles. We have some exciting brands within our client base that are keen to meet people within this new and evolving criteria.
Bookmark resources such as popular AI communities, competitions, and specialist job boards to keep your finger on the pulse and give yourself the best possible start in your exciting new career path.
