This is a full-time, remote position for someone who already holds an active AWS Certified Machine Learning credential and wants to put it to use on real projects rather than let it sit on a resume. There is no office attached to the role and no location requirement; you can work from anywhere.
The certification requirement is not a formality here. Teams hiring for this kind of role generally trust that credential to signal a working knowledge of the AWS machine learning stack, which frees up interview time to focus on how a candidate actually operates day to day.
The title says specialist, but the work is closer to a hybrid of technical delivery and light program management. You will not be building models from scratch every day. You will be planning, overseeing, and documenting machine learning work as it moves from idea to production.
Employers hiring for this seat are usually past the experimentation stage. They have a model, or several, already running in some form, and what they need is someone who can keep the surrounding process organized as those models scale up and touch more of the business, without letting anything quietly fall through the cracks along the way.
A bachelor's degree in a related field is the baseline education requirement. Three years of hands-on experience in machine learning work is expected, along with an active AWS Certified Machine Learning certification that is current, not expired or in progress.
Three years is enough time to have made real mistakes on a production system and learned something from them, which is part of why it is the bar rather than a lower number. Candidates who have only worked on academic or personal projects, without production exposure, tend to struggle with the oversight side of this role even when their technical skills are otherwise quite strong.
Documentation is not an afterthought in this role. A fair portion of the work is writing things down clearly enough that someone else could pick up a project without a long handoff meeting.
A model that performs well in testing but drifts once it hits live data is a common enough problem that this role often ends up owning the response to it: coordinating retraining schedules, documenting what changed, and making sure the right people know before performance dips too far.
The skills that matter most for this seat:
Remoteroles has listed a number of AWS-certified roles over the past year, and this one tends to draw candidates who came up through cloud engineering before shifting toward planning and oversight work. That path is not required, but it is common.
Communication holds this role together more than any single technical skill does. A data science team and a compliance team read the same project update very differently, and someone in this seat needs to write versions of that update that both sides actually find useful, without watering down what matters to either one.
The role pays 123,000 dollars annually, full-time. Health coverage, paid time off, and retirement plan matching are all included as standard.
That last item matters more than it might seem. AWS certifications expire and need to be renewed on a fixed cycle, and covering that cost is a direct, practical benefit rather than a vague perk.
Salary here reflects both the certification and the oversight responsibility that comes with the title. Reviews happen annually, and moving to a higher band typically depends on the scope of projects a person is trusted to run rather than years of tenure alone.
Work is largely asynchronous, organized around shared documents and periodic check-ins rather than a heavy meeting schedule. Deadlines are real and tracked, but the day-to-day pace stays even rather than swinging between idle and frantic.
A typical project might start with a stakeholder request for a new model to support a specific business decision. From there, this role tracks the work through data preparation, model development, testing, and deployment, checking in with the technical team at each stage without necessarily writing the code directly. Keeping that timeline visible to everyone involved is often the difference between a smooth rollout and a rushed one.
Risk tracking sits alongside that project work as a constant background task. A model that touches customer data or financial decisions carries risk that needs to be documented and reviewed on a schedule, not just noted once and forgotten. Missing that kind of review can create real problems well after a project is considered finished.
Submit a resume along with your AWS certification number or a way to verify it. Include a short summary of one machine learning project you helped plan or oversee, focusing on your role in coordinating the work rather than the technical build itself.
Review happens on a rolling basis. A first conversation typically covers your background and certification, and a second, if things move forward, walks through how you would approach a sample project timeline from kickoff to deployment.