ToneTag is the largest sound-wave communication technology platform on the planet that enables payments C proximity customer engagement services in different sectors like retail, mobility, restaurant ordering, and so on. It harnesses the power of sound to empower and enrich various businesses around the globe.

ToneTag has touched more than 55 Million consumers C 500,00 merchants in the payments space alone. It is an organization where innovation, hard work C fun go hand in hand to invent experiences that are unique in every sense. Our people are entrepreneurial C believe in going beyond today's problems to find tomorrow's solution.

If you are a seasoned professional looking for your next challenge, or just starting your career and looking for a company that created career-building opportunities, we offer ample scope as well as training to ensure that your skills C abilities reach their true potential. At ToneTag, we invite you to share our vision and commitment to achieving excellence in everything that we do!

Responsibilities :

- Design, develop, and implement machine learning models and algorithms.

- Create new models from scratch based on business requirements and data.

- Train, fine-tune, and evaluate machine learning models to ensure optimal performance.

- Deploy machine learning models into production environments.

- Measure and analyze the performance of machine learning models using metrics such as accuracy, precision, recall, F1 score, and ROC-AUC etc, and iteratively optimize models to meet business objectives.

- Conduct performance benchmarking and testing for deployed models, ensuring reliability and scalability in production environments.

- Collaborate with cross-functional teams to understand business needs and provide AI/ML solutions.

- Optimize and improve performance of existing models.

- Conduct research to identify new methodologies for applying AI/ML within the organization.

- Mentor junior engineers and provide technical guidance.

- Stay updated with the latest advancements in AI/ML technologies and methodologies.

Technical Skills :

- Machine Learning : Strong understanding of supervised, unsupervised, and reinforcement learning

techniques.

- Model Development : Experience in developing machine learning models from scratch, including data preprocessing, feature engineering, and model selection.

- Deep Learning : Proficiency with deep learning frameworks such as TensorFlow, PyTorch, or Keras.

- Natural Language Processing and generation : Experience with NLP techniques and tools, including large language models (LLMs) like GPT, BERT, llama etc.

- Programming : Proficiency in Python and familiarity with other languages such as Java or C++ is a plus.

- Tools and Libraries : Experience with ML libraries and tools such as Scikit-learn, Pandas, NumPy, and SciPy.

- Cloud Platforms : Experience with cloud-based ML platforms such as AWS SageMaker, Google Cloud AI, or Azure ML.

- MLOps : Knowledge of MLOps practices for model versioning, monitoring, and continuous integration/continuous deployment (CI/CD).

- Visualization : Proficiency with data visualization tools such as Matplotlib, Seaborn, or Tableau.

- Hugging Face : Familiarity with Hugging Face's ecosystem, including Transformers library for pre-trained models, datasets library for handling and processing datasets, and the Model Hub for sharing and discovering models.