We are seeking a Machine Learning Engineer to join our AI platform team. You will own the
end-to-end lifecycle of production ML systems: data pipeline construction, model training and
evaluation, and scalable inference deployment. Day-to-day work involves large-scale tabular and
unstructured data using Spark or Ray, experiment tracking in MLflow, and training PyTorch models
for classification and ranking tasks.

Requirements: 3+ years of applied ML engineering experience; strong Python skills with expertise
in PyTorch, scikit-learn, and Hugging Face Transformers; hands-on experience with feature stores
(Feast or Tecton), model registries, and A/B testing frameworks; familiarity with deploying
models via Triton Inference Server or SageMaker endpoints; understanding of ML monitoring
concepts (data drift, prediction drift, concept drift) using tools such as Evidently or
WhyLogs; experience with SQL and distributed compute (Spark, Dask). A track record of taking
models from notebook prototypes to latency-sensitive production services is essential.
