Machine Learning Developer

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 analyse the performance of machine learning models using metrics such as accuracy, precision, recall, F1 score, and ROC-AUC, etc, and iteratively optimise 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.

- Optimise and improve the performance of existing models.

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

- Mentor junior engineers and provide technical guidance.

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

Requirements :

- 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).

- Visualisation : Proficiency with data visualisation tools such as Matplotlib, Seaborn, or Tableau.

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

- Prefers folks from Tier 1 colleges.