Dear Hiring Manager,
I am returning to work after a planned career break and targeting ml engineer roles where my previous experience in training-serving consistency, latency work, drift monitoring, and release controls is directly useful. During the break I kept current through focused practice with Python, PyTorch, Spark, Airflow, feature stores, Docker, Kubernetes, and MLflow and rebuilt a recent sample around moved a fraud model from notebook handoff to monitored serving with feature freshness checks and shadow evaluation.
The break has not changed the way I approach the core work of this role. Your team needs production ML judgement, model serving, pipeline reliability, and monitoring discipline, and my strongest examples sit in that exact area. I would use this letter to show the connection with one specific project, the constraints I worked under, and the judgement I brought to the decision points.
A recent example is that I moved a fraud model from notebook handoff to monitored serving with feature freshness checks and shadow evaluation. That work required Python, PyTorch, Spark, Airflow, feature stores, Docker, Kubernetes, and MLflow, but the more important point is how I made decisions, explained tradeoffs, and followed the result through after release.
I would welcome the opportunity to discuss the role and the recent work I can show now. ML engineering teams need evidence that models survive production traffic, so I would keep the letter concise, evidence-led, and tied to the outcomes the hiring team is likely to care about.
Yours sincerely, Alex Morgan