how to find a MLE or AI engineer job by end of 2026? As mentioned earlier, there’s no guarantee that upcoming roles will be exactly similar over time. However, here are some strategies you can consider for maximizing your chances at finding an ML/AI Engineer role in the future (or until when):

1. Understand Current Roles and Responsibilities: Before moving forward with a career plan from 20 years ago onwards to today’s, it is crucial that you understand all aspects of past roles as they are not likely to change significantly over time due the rapid advancement in technology trends within AI/ML.

2. Practice Coding Problems: Even though we can’t predict specific future problems based on current tech advances, but solve a lot real-world coding questions which will help you understand how things work now at companies such as Google or Amazon and this could serve an excellent exercise for finding ML/AI Engineer roles.

3. Stay Updated: Technology is always evolving fast (often faster than human can adapt). So, keep yourself updated with the latest trends in AI / Machine Learning from past to present by current employers or industry professionals who are also interested in similar fields over time and participate actively within these communities.

4. Networking: Network is a very important skill for professional development as it can help you understand different perspectives, work with peers at your company (which will give opportunities), have discussions about new trends or technologies that might interest the person who shares yours in mind and where they could apply their knowledge to solve problems efficiently.

5. Building Projects: Even though we don’t predict future issues due tech advancements, but start building small scale projects at your company (often with a focus on real world challenges). This will give you hands-on experience and exposure where possible for the upcoming roles in AI/ML which would be more efficient than previous approaches.

Remember that finding ML /AI Engineer jobs by end of this year is not just an aim to achieve but also about ongoing learning, staying updated with current trends (and industry standards), applying your knowledge and skills at a fast-paced technological environment etc., which will give you the edge over upcoming roles. Also note: As AI/ML has seen rapid advancements in recent years due nature of big data applications that have wide reach across industries they are still not as mature or advanced for ML Engineers role to fill them with time (end-of year is also a great spot on this!).