Data Scientist /AI/ML

Job Description:

• 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).