Work on software design with team of data science and machine learning specialists
Support data scientists in deploying new models, monitoring performance and debugging issues
Optimize, measure and improve performances (latency, accuracy, drifts, etc.) of data science models in production , based off Tensorflow/MLeap (deep learning/neural network architecture) and other methodologies within Python and Scala ML ecosystem
Perform data engineering (ETL, pipeline orchestration, monitoring) on big data (Spark 2.x/3x), on large data sets specifically for data science
Transform ML model prototype into scalable, serve-able models for use in APIs and apps
Build and enforce operational excellence - monitoring, alerting, dashboards, resiliency, back up, auto remediation on failures, CI/CD
Drive AWS, Qubole and Databricks cost and performance optimization, alongside platform system
What You Should Bring
Must-have
Python, Scala, Apache Spark
SQL/NoSQL databases, ETL, big data
Knowledgeable in statistics
Experience with setting up CI/CD pipelines for ML and DS needs
Expertise in OOP, software design and best practices
Nice-to-have
AWS, Qubole, Databricks, Tensorflow/MLeap
Experience with learning-to-rank systems, deep learning, natural language processing, image processing
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