Qualifications
Experience in Machine Learning (ML), with deep expertise in writing, and reviewing production code in Python.
Knowledge of ML frameworks and libraries (such as TensorFlow/Pytorch), and exposure to various ML algorithms and their practical implementation in production at large scale.
Experience with designing and deploying scalable end-to-end Machine Learning/NLP systems as Databricks jobs/ dockerized containers
Experience on distributed, high throughput and low latency architecture.
Experience with NLP techniques around text cleaning/pre-processing, entity extraction, encoder-decoder architectures, similarity matching etc.
Experience with Prompt Engineering techniques
Understanding of LLM functionality and inference techniques
Experience building software on top of containerization technology (Kubernetes, Docker etc.), and familiarity with frameworks/tools such as FastAPI, Uvicorn.
Familiarity with Continuous Integration tools such as Jenkins.
Experience with architecting and consuming APIs in a scalable (multi-threaded/batched) fashion.
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