- 5 to 7 years of experience in machine learning engineering, with a significant track record in the developing production grade models and model pipelines in a regulated industry such as healthcare.
- Hands-on expertise in working with large language models (e.g., GPT, BERT), computer vision models, and classic NLP technologies.
- Proficient in programming languages such as Python, with extensive experience in ML libraries/frameworks like TensorFlow, PyTorch, OpenCV, etc.
- Strong understanding of deep learning techniques, model fine-tuning, hyper parameter optimization, and model optimization.
- Proven experience in deploying and managing ML models in production environments.
- Excellent analytical skills, with a problem-solving mindset and the ability to think strategically.
- Strong communication skills for articulating complex concepts to diverse audiences.
- Working knowledge or experience in MLOps and LLMOps using tools like mlflow, kubeflow.
- Working knowledge of basic software engineering principles and best practices.
- Demonstrated working knowledge and experience on classic ML techniques and frameworks.
Nice to have :
- Knowledge of Cloud vendor based ML Platforms such as Azure ML, Sagemaker.