Dear Hiring Manager,
I am applying to your graduate scheme on the machine learning engineering track, straight from my degree and ready to learn how models actually run in production. I understand a scheme is judging how quickly I can be taught and trusted, so I want to show a real project and an honest sense of how much I still have to pick up. My best evidence is a capstone where I moved a small model from a notebook into a monitored serving setup with basic feature checks and a shadow evaluation.
An entry-level hire needs to be coachable and useful before long, and I have aimed my project work at the gap between training a model and keeping it healthy in production. The scheme asks for production ML judgement, model serving, pipeline reliability, and monitoring discipline, and that capstone was my first real attempt at all four. I am not claiming polish, I am showing that I already care about the serving path, not just the offline score.
The project used Python, PyTorch, Docker, and a lightweight feature check, but the part worth your time is that I did not stop at a good validation number. I put the model behind a serving path, ran it in shadow against the existing logic, and watched for feature drift, which taught me how much production adds on top of the modelling. That is exactly the area I want to be mentored in.
I would be glad to talk through the serving setup, what broke along the way, and what I would want to learn first. ML engineering teams need evidence that models survive production traffic, so I will be honest about my level and clear about the direction I want to grow.
Yours sincerely, Alex Morgan