Description

You will architect distributed training strategies and manage the end-to-end lifecycle of machine learning models.

This role is on-site.

Responsibilities

• Architect distributed training strategies using frameworks like Horovod or DeepSpeed.
• Deploy and manage ML models using Docker, Kubernetes, and serving frameworks such as TensorFlow Serving, TorchServe, or Seldon Core.
• Implement model monitoring and drift detection systems alongside CI/CD pipelines for ML.
• Profile and optimize model performance for low-latency inference.
• Troubleshoot and debug complex distributed AI/ML systems and internal infrastructure.

Required Skills

• 5+ years of experience in AI/ML roles.
• Deep understanding of Supervised, Unsupervised, Deep Learning, and Reinforcement Learning.
• Expertise in Python and scientific libraries including NumPy and SciPy.
• Proficiency in TensorFlow and PyTorch ecosystems.
• Experience with data pipeline orchestration using Airflow or Kubeflow.
• Expertise in feature engineering platforms like Feast or Tecton.
• Solid understanding of distributed computing using Spark or Dask.
• Hands-on experience with Docker and Kubernetes.
• Knowledge of data serialization formats such as Parquet, Avro, and Protocol Buffers.

Key Skills

Education

Any Graduate