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AI / ML Engineer

We are looking for a Machine Learning Engineer with a specialization in Natural Language Processing (NLP) and Machine Learning Operations (MLOps) to join our rapidly growing team. The successful candidate will be a technical leader who is responsible for developing, deploying, and maintaining advanced NLP models in production environments. Your knowledge and insights will be key in driving our organization forward in the era of data-driven decision-making.

Responsibilities:

• Design, build, and deploy state-of-the-art NLP models to extract insights and automate processes from text data.

• Utilize and implement the latest research on transformer-based models (BERT, GPT-4, XLNet, T5, etc.), with expertise in using pre-trained models from Hugging Face.

• Fine-tune and deploy these models to serve specific business requirements.

• Develop scalable and efficient data pipelines for annotating, training, and validating machine learning models.

• Implement MLOps (Machine Learning Operations) practices to automate and improve the ML lifecycle, including data collection, model development, deployment, monitoring, and maintenance.

• Work closely with other data scientists, engineers, and product managers to integrate machine learning solutions into the company’s products and services.

• Stay updated with the latest developments in the field of NLP, and apply them in problem-solving.

• Provide support in setting up, maintaining, and evolving our ML infrastructure.

Requirements:

• Bachelor's or Master's degree in Computer Science, Data Science, or a related quantitative field.

• 3 years min of experience

• Deep understanding and hands-on experience in NLP, with a focus on transformer models and contextual embeddings.

• Proven experience in utilizing, fine-tuning, and deploying pre-trained models from Hugging Face.

• Proficiency in Python and its data science ecosystem (NumPy, pandas, scikit-learn).

• Proficiency in distributed computing frameworks such as Dask and Ray.

• Experience with parallel processing techniques for efficient data processing.

• Knowledge of data visualization tools and libraries (e.g., Matplotlib, Plotly, Tableau).

• Experience in using modern machine learning libraries (e.g., TensorFlow, PyTorch).