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Senior AI/ML Developer
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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 the 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
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).
- Visualization : Proficiency with data visualization 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.
