Data Scientist

Architect and implement distributed training strategies utilizing frameworks like Horovod or DeepSpeed.
Deploy and manage ML models using containerization (Docker, Kubernetes) and serving frameworks (TensorFlow Serving, TorchServe, Seldon Core).
Implement robust model monitoring and drift detection systems.
Leverage MLOps best practices for CI/CD of ML pipelines and models.
Profile and optimize model performance for low-latency inference.
Integrate with various data storage solutions (e.g., distributed file systems, vector databases).
Contribute to the development of internal AI/ML infrastructure and tooling.
Troubleshoot and debug complex distributed AI/ML systems.
Key Skills:Deep understanding of Machine Learning paradigms (Supervised, Unsupervised, Deep Learning, Reinforcement Learning).
Expertise in Python and relevant scientific computing libraries (NumPy, SciPy).
Proficient in deep learning frameworks (TensorFlow, PyTorch) and their ecosystems.
Strong experience with data pipeline orchestration tools (Airflow, Kubeflow).
Expertise in feature engineering platforms (Feast, Tecton).
Solid understanding of distributed computing concepts and frameworks (Spark, Dask).
Experience with containerization and orchestration (Docker, Kubernetes).
Knowledge of ML model serving frameworks (TF Serving, TorchServe, Seldon Core).
Familiarity with model monitoring and drift detection techniques.
Strong understanding of data serialization and storage formats (e.g., Parquet, Avro, Protocol Buffers).