How to Get an AI or Machine Learning Engineer Job

AI hiring has grown quickly, but the job titles can be confusing. "AI engineer", "machine learning engineer" and "MLOps engineer" often describe different work. Knowing the difference helps you target the right roles.

Three common role types

• AI engineer (applied LLM work): builds products on top of large language models: retrieval-augmented generation, agents, evaluation pipelines and prompt tooling. Strong software engineering matters more than deep research experience.
• Machine learning engineer: trains, tunes and serves models such as recommendation, ranking, forecasting or computer vision. Listings ask for PyTorch or TensorFlow, feature pipelines and model evaluation.
• MLOps / ML platform engineer: builds the infrastructure for training and serving: GPUs, Kubernetes, model registries, monitoring and CI/CD for models.

Skills that appear again and again

Python is close to universal. Beyond that, employers commonly list SQL, a deep learning framework, cloud experience (AWS, GCP or Azure), vector databases for LLM applications, and experience putting models into production rather than only in notebooks.

Making the move from software engineering

Backend and data engineers are well placed to move into applied AI roles. A practical route is to ship one end-to-end project: an LLM-powered feature with retrieval, an evaluation set, and monitoring for cost and quality. Being able to discuss how you measured whether it worked is exactly what interviewers probe.

Read the listing for seniority and location

Many AI roles are senior and some require relocation or on-site work near research teams. Check the location requirements and whether the role is closer to research or to product engineering before you apply.

Where to look

Browse AI and machine learning jobs on devo.zone, or go straight to remote AI and ML jobs. For data-heavy roles, also check remote data engineer and data scientist jobs.