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.
(ref : hirist.tech)
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