AI Ranking Systems Engineer
Budget: $25 – $50 USD
I’m rolling out a new, production-grade recommendation and ranking pipeline for several enterprise products and I need a senior-level engineer who already lives and breathes these systems. Your primary playground will be large-scale feature engineering and EDA, then building, tuning and shipping models that actually move CTR/CVR metrics in the wild.
Here’s the environment you’ll be stepping into: Python and Spark for data prep, TensorFlow/PyTorch for model work, all running on AWS — S3, EMR and SageMaker. GenAI/LLM/RAG methods are in scope; I’m especially interested in seeing how you weave retrieval-augmented generation into ranking logic.
You should be comfortable:
• Designing and maintaining a feature store, then creating training/validation sets
• Building custom neural networks as well as refining pre-trained models
• Squeezing every millisecond and metric point out of model performance
• Packaging and deploying to SageMaker endpoints with robust monitoring
• Demonstrating lift through clear offline metrics (NDCG, MAP, AUC) and online A/B tests
Send me your latest CV and a link to your LinkedIn profile so we can fast-track the conversation. In your note, cite one ranking project you owned end-to-end and which AWS tools you leaned on.
Here’s the environment you’ll be stepping into: Python and Spark for data prep, TensorFlow/PyTorch for model work, all running on AWS — S3, EMR and SageMaker. GenAI/LLM/RAG methods are in scope; I’m especially interested in seeing how you weave retrieval-augmented generation into ranking logic.
You should be comfortable:
• Designing and maintaining a feature store, then creating training/validation sets
• Building custom neural networks as well as refining pre-trained models
• Squeezing every millisecond and metric point out of model performance
• Packaging and deploying to SageMaker endpoints with robust monitoring
• Demonstrating lift through clear offline metrics (NDCG, MAP, AUC) and online A/B tests
Send me your latest CV and a link to your LinkedIn profile so we can fast-track the conversation. In your note, cite one ranking project you owned end-to-end and which AWS tools you leaned on.