How you'll make an impact?
- Design and develop scalable training and inference platforms.
- Develop and Integrate data pre and post processing workflows for ML models.
- Develop model monitoring service on top of inference platform.
- Develop platforms to grade models at scale.
- Develop model experimentation tools.
- Develop sampling strategy for assessing model performance.
- Oversee the entire lifecycle (design, experimentation, development, deployment, monitoring, and maintenance).
- Create reusable workflows for the Data Science Models and integrate them with the production features while avoiding redundancies.
- Deploy code to production and engage in code reviews.
- Refactor service to improve code quality, runtime efficiency and resource optimisation.
- Develop automation / active Learning frameworks for retraining models.
- Lead and mentor a team of software engineers: Foster a collaborative and high-performing team environment, providing guidance and support to your engineers to help them achieve their full potential.
What we're looking for?
- Bachelor's / Master’s Degree in Computer Science, Mathematics & Computing, Electrical Engineering, or a related field with a min of 8+ years of experience.
- Experience with machine learning ecosystem.
- Rock solid experience in monitoring models and data in a production setup.
- Experience in sampling strategies for different types of models and use cases.
- Experience in grading models at scale.
- Experience in designing and developing distributed training and inference platforms using distributed computing (PySpark, Kubeflow, Kubernetes).
- Extensive experience in writing computer programs in Python.
- Experience with Docker.
- Experience in developing tools to evaluate model performance (MLFlow,Tensorboard,Wandb).
- Experience in MLOps (AWS, GCP, Azure ).
- Experience in handling large datasets for training (HDFS,Datalakes,NoSQL/SQL).
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